vLLM inference performance testing

2025-09-11

53 min read time

Applies to Linux

The ROCm vLLM Docker image offers a prebuilt, optimized environment for validating large language model (LLM) inference performance on AMD Instinct™ MI300X series accelerators. This ROCm vLLM Docker image integrates vLLM and PyTorch tailored specifically for MI300X series accelerators and includes the following components:

Software component	Version
ROCm	6.4.1
vLLM	0.10.1 (0.10.1rc2.dev409+g0b6bf6691.rocm641)
PyTorch	2.7.0+gitf717b2a
hipBLASLt	0.15

With this Docker image, you can quickly test the expected inference performance numbers for MI300X series accelerators.

What’s new

The following is summary of notable changes since the previous ROCm/vLLM Docker release.

• Upgraded to vLLM v0.10.1.

• Set VLLM_V1_USE_PREFILL_DECODE_ATTENTION=1 by default for better performance.

• Set VLLM_ROCM_USE_AITER_RMSNORM=0 by default to avoid various issues with torch compile.

Supported models

The following models are supported for inference performance benchmarking with vLLM and ROCm. Some instructions, commands, and recommendations in this documentation might vary by model – select one to get started.

Model

Meta Llama

Mistral AI

Qwen

Microsoft Phi

Variant

Llama 3.1 8B

Llama 3.1 70B

Llama 3.1 405B

Llama 2 70B

Llama 3.1 8B FP8

Llama 3.1 70B FP8

Llama 3.1 405B FP8

Mixtral MoE 8x7B

Mixtral MoE 8x22B

Mixtral MoE 8x7B FP8

Mixtral MoE 8x22B FP8

QwQ-32B

Qwen3 30B A3B

Phi-4

Note

See the Llama 3.1 8B model card on Hugging Face to learn more about your selected model. Some models require access authorization prior to use via an external license agreement through a third party.

Note

See the Llama 3.1 70B model card on Hugging Face to learn more about your selected model. Some models require access authorization prior to use via an external license agreement through a third party.

Note

See the Llama 3.1 405B model card on Hugging Face to learn more about your selected model. Some models require access authorization prior to use via an external license agreement through a third party.

Note

See the Llama 2 70B model card on Hugging Face to learn more about your selected model. Some models require access authorization prior to use via an external license agreement through a third party.

Note

See the Llama 3.1 8B FP8 model card on Hugging Face to learn more about your selected model. Some models require access authorization prior to use via an external license agreement through a third party.

This model uses FP8 quantization via AMD Quark for efficient inference on AMD accelerators.

Note

See the Llama 3.1 70B FP8 model card on Hugging Face to learn more about your selected model. Some models require access authorization prior to use via an external license agreement through a third party.

This model uses FP8 quantization via AMD Quark for efficient inference on AMD accelerators.

Note

See the Llama 3.1 405B FP8 model card on Hugging Face to learn more about your selected model. Some models require access authorization prior to use via an external license agreement through a third party.

This model uses FP8 quantization via AMD Quark for efficient inference on AMD accelerators.

Note

See the Mixtral MoE 8x7B model card on Hugging Face to learn more about your selected model. Some models require access authorization prior to use via an external license agreement through a third party.

Note

See the Mixtral MoE 8x22B model card on Hugging Face to learn more about your selected model. Some models require access authorization prior to use via an external license agreement through a third party.

Note

See the Mixtral MoE 8x7B FP8 model card on Hugging Face to learn more about your selected model. Some models require access authorization prior to use via an external license agreement through a third party.

This model uses FP8 quantization via AMD Quark for efficient inference on AMD accelerators.

Note

See the Mixtral MoE 8x22B FP8 model card on Hugging Face to learn more about your selected model. Some models require access authorization prior to use via an external license agreement through a third party.

This model uses FP8 quantization via AMD Quark for efficient inference on AMD accelerators.

Note

See the QwQ-32B model card on Hugging Face to learn more about your selected model. Some models require access authorization prior to use via an external license agreement through a third party.

Note

See the Qwen3 30B A3B model card on Hugging Face to learn more about your selected model. Some models require access authorization prior to use via an external license agreement through a third party.

Note

See the Phi-4 model card on Hugging Face to learn more about your selected model. Some models require access authorization prior to use via an external license agreement through a third party.

Performance measurements

To evaluate performance, the Performance results with AMD ROCm software page provides reference throughput and serving measurements for inferencing popular AI models.

Important

The performance data presented in Performance results with AMD ROCm software only reflects the latest version of this inference benchmarking environment. The listed measurements should not be interpreted as the peak performance achievable by AMD Instinct MI325X and MI300X accelerators or ROCm software.

System validation

Before running AI workloads, it’s important to validate that your AMD hardware is configured correctly and performing optimally.

If you have already validated your system settings, including aspects like NUMA auto-balancing, you can skip this step. Otherwise, complete the procedures in the System validation and optimization guide to properly configure your system settings before starting training.

To test for optimal performance, consult the recommended System health benchmarks. This suite of tests will help you verify and fine-tune your system’s configuration.

Pull the Docker image

Download the ROCm vLLM Docker image. Use the following command to pull the Docker image from Docker Hub.

docker pull rocm/vllm:rocm6.4.1_vllm_0.10.1_20250909

Benchmarking

Once the setup is complete, choose between two options to reproduce the benchmark results:

MAD-integrated benchmarking

The following run command is tailored to Llama 3.1 8B. See Supported models to switch to another available model.

1. Clone the ROCm Model Automation and Dashboarding (ROCm/MAD) repository to a local directory and install the required packages on the host machine.

   git clone https://github.com/ROCm/MAD
   cd MAD
   pip install -r requirements.txt
   

2. Use this command to run the performance benchmark test on the Llama 3.1 8B model using one GPU with the float16 data type on the host machine.

   export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
   madengine run \
       --tags pyt_vllm_llama-3.1-8b \
       --keep-model-dir \
       --live-output \
       --timeout 28800
   

MAD launches a Docker container with the name container_ci-pyt_vllm_llama-3.1-8b. The throughput and serving reports of the model are collected in the following paths: pyt_vllm_llama-3.1-8b_throughput.csv and pyt_vllm_llama-3.1-8b_serving.csv.

Although the available models are preconfigured to collect offline throughput and online serving performance data, you can also change the benchmarking parameters. See the standalone benchmarking tab for more information.

Standalone benchmarking

The following commands are optimized for Llama 3.1 8B. See Supported models to switch to another available model.

See also

For more information on configuration, see the config files in the MAD repository. Refer to the vLLM engine for descriptions of available configuration options and Benchmarking vLLM for additional benchmarking information.

Launch the container

You can run the vLLM benchmark tool independently by starting the Docker container as shown in the following snippet.

docker pull rocm/vllm:rocm6.4.1_vllm_0.10.1_20250909
docker run -it \
    --device=/dev/kfd \
    --device=/dev/dri \
    --group-add video \
    --shm-size 16G \
    --security-opt seccomp=unconfined \
    --security-opt apparmor=unconfined \
    --cap-add=SYS_PTRACE \
    -v $(pwd):/workspace \
    --env HUGGINGFACE_HUB_CACHE=/workspace \
    --name test \
    rocm/vllm:rocm6.4.1_vllm_0.10.1_20250909

Throughput command

Use the following command to start the throughput benchmark.

model=meta-llama/Llama-3.1-8B-Instruct
tp=1
num_prompts=1024
in=128
out=128
dtype=auto
kv_cache_dtype=auto
max_num_seqs=1024
max_seq_len_to_capture=131072
max_num_batched_tokens=131072
max_model_len=8192

vllm bench throughput --model $model \
    -tp $tp \
    --num-prompts $num_prompts \
    --input-len $in \
    --output-len $out \
    --dtype $dtype \
    --kv-cache-dtype $kv_cache_dtype \
    --max-num-seqs $max_num_seqs \
    --max-seq-len-to-capture $max_seq_len_to_capture \
    --max-num-batched-tokens $max_num_batched_tokens \
    --max-model-len $max_model_len \
    --trust-remote-code \
    --output-json ${model}_throughput.json \
    --gpu-memory-utilization 0.9

Serving command

1. Start the server using the following command:

   model=meta-llama/Llama-3.1-8B-Instruct
   tp=1
   dtype=auto
   kv_cache_dtype=auto
   max_num_seqs=256
   max_seq_len_to_capture=131072
   max_num_batched_tokens=131072
   max_model_len=8192
   
   vllm serve $model \
       -tp $tp \
       --dtype $dtype \
       --kv-cache-dtype $kv_cache_dtype \
       --max-num-seqs $max_num_seqs \
       --max-seq-len-to-capture $max_seq_len_to_capture \
       --max-num-batched-tokens $max_num_batched_tokens \
       --max-model-len $max_model_len \
       --no-enable-prefix-caching \
       --swap-space 16 \
       --disable-log-requests \
       --trust-remote-code \
       --gpu-memory-utilization 0.9
   

   Wait until the model has loaded and the server is ready to accept requests.

2. On another terminal on the same machine, run the benchmark:

   # Connect to the container
   docker exec -it test bash
   
   # Wait for the server to start
   until curl -s http://localhost:8000/v1/models; do sleep 30; done
   
   # Run the benchmark
   model=meta-llama/Llama-3.1-8B-Instruct
   max_concurrency=1
   num_prompts=10
   in=128
   out=128
   vllm bench serve --model $model \
       --percentile-metrics "ttft,tpot,itl,e2el" \
       --dataset-name random \
       --ignore-eos \
       --max-concurrency $max_concurrency \
       --num-prompts $num_prompts \
       --random-input-len $in \
       --random-output-len $out \
       --trust-remote-code \
       --save-result \
       --result-filename ${model}_serving.json
   

Note

If you encounter the following error, pass your access-authorized Hugging Face token to the gated models.

OSError: You are trying to access a gated repo.

# pass your HF_TOKEN
export HF_TOKEN=$your_personal_hf_token

Note

Throughput is calculated as:

• \[throughput\_tot = requests \times (\mathsf{\text{input lengths}} + \mathsf{\text{output lengths}}) / elapsed\_time\]
• \[throughput\_gen = requests \times \mathsf{\text{output lengths}} / elapsed\_time\]

MAD-integrated benchmarking

The following run command is tailored to Llama 3.1 70B. See Supported models to switch to another available model.

1. Clone the ROCm Model Automation and Dashboarding (ROCm/MAD) repository to a local directory and install the required packages on the host machine.

   git clone https://github.com/ROCm/MAD
   cd MAD
   pip install -r requirements.txt
   

2. Use this command to run the performance benchmark test on the Llama 3.1 70B model using one GPU with the float16 data type on the host machine.

   export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
   madengine run \
       --tags pyt_vllm_llama-3.1-70b \
       --keep-model-dir \
       --live-output \
       --timeout 28800
   

MAD launches a Docker container with the name container_ci-pyt_vllm_llama-3.1-70b. The throughput and serving reports of the model are collected in the following paths: pyt_vllm_llama-3.1-70b_throughput.csv and pyt_vllm_llama-3.1-70b_serving.csv.

Although the available models are preconfigured to collect offline throughput and online serving performance data, you can also change the benchmarking parameters. See the standalone benchmarking tab for more information.

Standalone benchmarking

The following commands are optimized for Llama 3.1 70B. See Supported models to switch to another available model.

See also

For more information on configuration, see the config files in the MAD repository. Refer to the vLLM engine for descriptions of available configuration options and Benchmarking vLLM for additional benchmarking information.

Launch the container

You can run the vLLM benchmark tool independently by starting the Docker container as shown in the following snippet.

docker pull rocm/vllm:rocm6.4.1_vllm_0.10.1_20250909
docker run -it \
    --device=/dev/kfd \
    --device=/dev/dri \
    --group-add video \
    --shm-size 16G \
    --security-opt seccomp=unconfined \
    --security-opt apparmor=unconfined \
    --cap-add=SYS_PTRACE \
    -v $(pwd):/workspace \
    --env HUGGINGFACE_HUB_CACHE=/workspace \
    --name test \
    rocm/vllm:rocm6.4.1_vllm_0.10.1_20250909

Throughput command

Use the following command to start the throughput benchmark.

model=meta-llama/Llama-3.1-70B-Instruct
tp=8
num_prompts=1024
in=128
out=128
dtype=auto
kv_cache_dtype=auto
max_num_seqs=1024
max_seq_len_to_capture=131072
max_num_batched_tokens=131072
max_model_len=8192

vllm bench throughput --model $model \
    -tp $tp \
    --num-prompts $num_prompts \
    --input-len $in \
    --output-len $out \
    --dtype $dtype \
    --kv-cache-dtype $kv_cache_dtype \
    --max-num-seqs $max_num_seqs \
    --max-seq-len-to-capture $max_seq_len_to_capture \
    --max-num-batched-tokens $max_num_batched_tokens \
    --max-model-len $max_model_len \
    --trust-remote-code \
    --output-json ${model}_throughput.json \
    --gpu-memory-utilization 0.9

Serving command

1. Start the server using the following command:

   model=meta-llama/Llama-3.1-70B-Instruct
   tp=8
   dtype=auto
   kv_cache_dtype=auto
   max_num_seqs=256
   max_seq_len_to_capture=131072
   max_num_batched_tokens=131072
   max_model_len=8192
   
   vllm serve $model \
       -tp $tp \
       --dtype $dtype \
       --kv-cache-dtype $kv_cache_dtype \
       --max-num-seqs $max_num_seqs \
       --max-seq-len-to-capture $max_seq_len_to_capture \
       --max-num-batched-tokens $max_num_batched_tokens \
       --max-model-len $max_model_len \
       --no-enable-prefix-caching \
       --swap-space 16 \
       --disable-log-requests \
       --trust-remote-code \
       --gpu-memory-utilization 0.9
   

   Wait until the model has loaded and the server is ready to accept requests.

2. On another terminal on the same machine, run the benchmark:

   # Connect to the container
   docker exec -it test bash
   
   # Wait for the server to start
   until curl -s http://localhost:8000/v1/models; do sleep 30; done
   
   # Run the benchmark
   model=meta-llama/Llama-3.1-70B-Instruct
   max_concurrency=1
   num_prompts=10
   in=128
   out=128
   vllm bench serve --model $model \
       --percentile-metrics "ttft,tpot,itl,e2el" \
       --dataset-name random \
       --ignore-eos \
       --max-concurrency $max_concurrency \
       --num-prompts $num_prompts \
       --random-input-len $in \
       --random-output-len $out \
       --trust-remote-code \
       --save-result \
       --result-filename ${model}_serving.json
   

Note

If you encounter the following error, pass your access-authorized Hugging Face token to the gated models.

OSError: You are trying to access a gated repo.

# pass your HF_TOKEN
export HF_TOKEN=$your_personal_hf_token

Note

Throughput is calculated as:

• \[throughput\_tot = requests \times (\mathsf{\text{input lengths}} + \mathsf{\text{output lengths}}) / elapsed\_time\]
• \[throughput\_gen = requests \times \mathsf{\text{output lengths}} / elapsed\_time\]

MAD-integrated benchmarking

The following run command is tailored to Llama 3.1 405B. See Supported models to switch to another available model.

1. Clone the ROCm Model Automation and Dashboarding (ROCm/MAD) repository to a local directory and install the required packages on the host machine.

   git clone https://github.com/ROCm/MAD
   cd MAD
   pip install -r requirements.txt
   

2. Use this command to run the performance benchmark test on the Llama 3.1 405B model using one GPU with the float16 data type on the host machine.

   export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
   madengine run \
       --tags pyt_vllm_llama-3.1-405b \
       --keep-model-dir \
       --live-output \
       --timeout 28800
   

MAD launches a Docker container with the name container_ci-pyt_vllm_llama-3.1-405b. The throughput and serving reports of the model are collected in the following paths: pyt_vllm_llama-3.1-405b_throughput.csv and pyt_vllm_llama-3.1-405b_serving.csv.

Although the available models are preconfigured to collect offline throughput and online serving performance data, you can also change the benchmarking parameters. See the standalone benchmarking tab for more information.

Standalone benchmarking

The following commands are optimized for Llama 3.1 405B. See Supported models to switch to another available model.

See also

For more information on configuration, see the config files in the MAD repository. Refer to the vLLM engine for descriptions of available configuration options and Benchmarking vLLM for additional benchmarking information.

Launch the container

You can run the vLLM benchmark tool independently by starting the Docker container as shown in the following snippet.

docker pull rocm/vllm:rocm6.4.1_vllm_0.10.1_20250909
docker run -it \
    --device=/dev/kfd \
    --device=/dev/dri \
    --group-add video \
    --shm-size 16G \
    --security-opt seccomp=unconfined \
    --security-opt apparmor=unconfined \
    --cap-add=SYS_PTRACE \
    -v $(pwd):/workspace \
    --env HUGGINGFACE_HUB_CACHE=/workspace \
    --name test \
    rocm/vllm:rocm6.4.1_vllm_0.10.1_20250909

Throughput command

Use the following command to start the throughput benchmark.

model=meta-llama/Llama-3.1-405B-Instruct
tp=8
num_prompts=1024
in=128
out=128
dtype=auto
kv_cache_dtype=auto
max_num_seqs=1024
max_seq_len_to_capture=131072
max_num_batched_tokens=131072
max_model_len=8192

vllm bench throughput --model $model \
    -tp $tp \
    --num-prompts $num_prompts \
    --input-len $in \
    --output-len $out \
    --dtype $dtype \
    --kv-cache-dtype $kv_cache_dtype \
    --max-num-seqs $max_num_seqs \
    --max-seq-len-to-capture $max_seq_len_to_capture \
    --max-num-batched-tokens $max_num_batched_tokens \
    --max-model-len $max_model_len \
    --trust-remote-code \
    --output-json ${model}_throughput.json \
    --gpu-memory-utilization 0.9

Serving command

1. Start the server using the following command:

   model=meta-llama/Llama-3.1-405B-Instruct
   tp=8
   dtype=auto
   kv_cache_dtype=auto
   max_num_seqs=256
   max_seq_len_to_capture=131072
   max_num_batched_tokens=131072
   max_model_len=8192
   
   vllm serve $model \
       -tp $tp \
       --dtype $dtype \
       --kv-cache-dtype $kv_cache_dtype \
       --max-num-seqs $max_num_seqs \
       --max-seq-len-to-capture $max_seq_len_to_capture \
       --max-num-batched-tokens $max_num_batched_tokens \
       --max-model-len $max_model_len \
       --no-enable-prefix-caching \
       --swap-space 16 \
       --disable-log-requests \
       --trust-remote-code \
       --gpu-memory-utilization 0.9
   

   Wait until the model has loaded and the server is ready to accept requests.

2. On another terminal on the same machine, run the benchmark:

   # Connect to the container
   docker exec -it test bash
   
   # Wait for the server to start
   until curl -s http://localhost:8000/v1/models; do sleep 30; done
   
   # Run the benchmark
   model=meta-llama/Llama-3.1-405B-Instruct
   max_concurrency=1
   num_prompts=10
   in=128
   out=128
   vllm bench serve --model $model \
       --percentile-metrics "ttft,tpot,itl,e2el" \
       --dataset-name random \
       --ignore-eos \
       --max-concurrency $max_concurrency \
       --num-prompts $num_prompts \
       --random-input-len $in \
       --random-output-len $out \
       --trust-remote-code \
       --save-result \
       --result-filename ${model}_serving.json
   

Note

If you encounter the following error, pass your access-authorized Hugging Face token to the gated models.

OSError: You are trying to access a gated repo.

# pass your HF_TOKEN
export HF_TOKEN=$your_personal_hf_token

Note

Throughput is calculated as:

• \[throughput\_tot = requests \times (\mathsf{\text{input lengths}} + \mathsf{\text{output lengths}}) / elapsed\_time\]
• \[throughput\_gen = requests \times \mathsf{\text{output lengths}} / elapsed\_time\]

MAD-integrated benchmarking

The following run command is tailored to Llama 2 70B. See Supported models to switch to another available model.

1. Clone the ROCm Model Automation and Dashboarding (ROCm/MAD) repository to a local directory and install the required packages on the host machine.

   git clone https://github.com/ROCm/MAD
   cd MAD
   pip install -r requirements.txt
   

2. Use this command to run the performance benchmark test on the Llama 2 70B model using one GPU with the float16 data type on the host machine.

   export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
   madengine run \
       --tags pyt_vllm_llama-2-70b \
       --keep-model-dir \
       --live-output \
       --timeout 28800
   

MAD launches a Docker container with the name container_ci-pyt_vllm_llama-2-70b. The throughput and serving reports of the model are collected in the following paths: pyt_vllm_llama-2-70b_throughput.csv and pyt_vllm_llama-2-70b_serving.csv.

Although the available models are preconfigured to collect offline throughput and online serving performance data, you can also change the benchmarking parameters. See the standalone benchmarking tab for more information.

Standalone benchmarking

The following commands are optimized for Llama 2 70B. See Supported models to switch to another available model.

See also

For more information on configuration, see the config files in the MAD repository. Refer to the vLLM engine for descriptions of available configuration options and Benchmarking vLLM for additional benchmarking information.

Launch the container

You can run the vLLM benchmark tool independently by starting the Docker container as shown in the following snippet.

docker pull rocm/vllm:rocm6.4.1_vllm_0.10.1_20250909
docker run -it \
    --device=/dev/kfd \
    --device=/dev/dri \
    --group-add video \
    --shm-size 16G \
    --security-opt seccomp=unconfined \
    --security-opt apparmor=unconfined \
    --cap-add=SYS_PTRACE \
    -v $(pwd):/workspace \
    --env HUGGINGFACE_HUB_CACHE=/workspace \
    --name test \
    rocm/vllm:rocm6.4.1_vllm_0.10.1_20250909

Throughput command

Use the following command to start the throughput benchmark.

model=meta-llama/Llama-2-70b-chat-hf
tp=8
num_prompts=1024
in=128
out=128
dtype=auto
kv_cache_dtype=auto
max_num_seqs=1024
max_seq_len_to_capture=4096
max_num_batched_tokens=4096
max_model_len=4096

vllm bench throughput --model $model \
    -tp $tp \
    --num-prompts $num_prompts \
    --input-len $in \
    --output-len $out \
    --dtype $dtype \
    --kv-cache-dtype $kv_cache_dtype \
    --max-num-seqs $max_num_seqs \
    --max-seq-len-to-capture $max_seq_len_to_capture \
    --max-num-batched-tokens $max_num_batched_tokens \
    --max-model-len $max_model_len \
    --trust-remote-code \
    --output-json ${model}_throughput.json \
    --gpu-memory-utilization 0.9

Serving command

1. Start the server using the following command:

   model=meta-llama/Llama-2-70b-chat-hf
   tp=8
   dtype=auto
   kv_cache_dtype=auto
   max_num_seqs=256
   max_seq_len_to_capture=4096
   max_num_batched_tokens=4096
   max_model_len=4096
   
   vllm serve $model \
       -tp $tp \
       --dtype $dtype \
       --kv-cache-dtype $kv_cache_dtype \
       --max-num-seqs $max_num_seqs \
       --max-seq-len-to-capture $max_seq_len_to_capture \
       --max-num-batched-tokens $max_num_batched_tokens \
       --max-model-len $max_model_len \
       --no-enable-prefix-caching \
       --swap-space 16 \
       --disable-log-requests \
       --trust-remote-code \
       --gpu-memory-utilization 0.9
   

   Wait until the model has loaded and the server is ready to accept requests.

2. On another terminal on the same machine, run the benchmark:

   # Connect to the container
   docker exec -it test bash
   
   # Wait for the server to start
   until curl -s http://localhost:8000/v1/models; do sleep 30; done
   
   # Run the benchmark
   model=meta-llama/Llama-2-70b-chat-hf
   max_concurrency=1
   num_prompts=10
   in=128
   out=128
   vllm bench serve --model $model \
       --percentile-metrics "ttft,tpot,itl,e2el" \
       --dataset-name random \
       --ignore-eos \
       --max-concurrency $max_concurrency \
       --num-prompts $num_prompts \
       --random-input-len $in \
       --random-output-len $out \
       --trust-remote-code \
       --save-result \
       --result-filename ${model}_serving.json
   

Note

If you encounter the following error, pass your access-authorized Hugging Face token to the gated models.

OSError: You are trying to access a gated repo.

# pass your HF_TOKEN
export HF_TOKEN=$your_personal_hf_token

Note

Throughput is calculated as:

• \[throughput\_tot = requests \times (\mathsf{\text{input lengths}} + \mathsf{\text{output lengths}}) / elapsed\_time\]
• \[throughput\_gen = requests \times \mathsf{\text{output lengths}} / elapsed\_time\]

MAD-integrated benchmarking

The following run command is tailored to Llama 3.1 8B FP8. See Supported models to switch to another available model.

1. Clone the ROCm Model Automation and Dashboarding (ROCm/MAD) repository to a local directory and install the required packages on the host machine.

   git clone https://github.com/ROCm/MAD
   cd MAD
   pip install -r requirements.txt
   

2. Use this command to run the performance benchmark test on the Llama 3.1 8B FP8 model using one GPU with the float8 data type on the host machine.

   export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
   madengine run \
       --tags pyt_vllm_llama-3.1-8b_fp8 \
       --keep-model-dir \
       --live-output \
       --timeout 28800
   

MAD launches a Docker container with the name container_ci-pyt_vllm_llama-3.1-8b_fp8. The throughput and serving reports of the model are collected in the following paths: pyt_vllm_llama-3.1-8b_fp8_throughput.csv and pyt_vllm_llama-3.1-8b_fp8_serving.csv.

Although the available models are preconfigured to collect offline throughput and online serving performance data, you can also change the benchmarking parameters. See the standalone benchmarking tab for more information.

Standalone benchmarking

The following commands are optimized for Llama 3.1 8B FP8. See Supported models to switch to another available model.

See also

For more information on configuration, see the config files in the MAD repository. Refer to the vLLM engine for descriptions of available configuration options and Benchmarking vLLM for additional benchmarking information.

Launch the container

You can run the vLLM benchmark tool independently by starting the Docker container as shown in the following snippet.

docker pull rocm/vllm:rocm6.4.1_vllm_0.10.1_20250909
docker run -it \
    --device=/dev/kfd \
    --device=/dev/dri \
    --group-add video \
    --shm-size 16G \
    --security-opt seccomp=unconfined \
    --security-opt apparmor=unconfined \
    --cap-add=SYS_PTRACE \
    -v $(pwd):/workspace \
    --env HUGGINGFACE_HUB_CACHE=/workspace \
    --name test \
    rocm/vllm:rocm6.4.1_vllm_0.10.1_20250909

Throughput command

Use the following command to start the throughput benchmark.

model=amd/Llama-3.1-8B-Instruct-FP8-KV
tp=1
num_prompts=1024
in=128
out=128
dtype=auto
kv_cache_dtype=fp8
max_num_seqs=1024
max_seq_len_to_capture=131072
max_num_batched_tokens=131072
max_model_len=8192

vllm bench throughput --model $model \
    -tp $tp \
    --num-prompts $num_prompts \
    --input-len $in \
    --output-len $out \
    --dtype $dtype \
    --kv-cache-dtype $kv_cache_dtype \
    --max-num-seqs $max_num_seqs \
    --max-seq-len-to-capture $max_seq_len_to_capture \
    --max-num-batched-tokens $max_num_batched_tokens \
    --max-model-len $max_model_len \
    --trust-remote-code \
    --output-json ${model}_throughput.json \
    --gpu-memory-utilization 0.9

Serving command

1. Start the server using the following command:

   model=amd/Llama-3.1-8B-Instruct-FP8-KV
   tp=1
   dtype=auto
   kv_cache_dtype=fp8
   max_num_seqs=256
   max_seq_len_to_capture=131072
   max_num_batched_tokens=131072
   max_model_len=8192
   
   vllm serve $model \
       -tp $tp \
       --dtype $dtype \
       --kv-cache-dtype $kv_cache_dtype \
       --max-num-seqs $max_num_seqs \
       --max-seq-len-to-capture $max_seq_len_to_capture \
       --max-num-batched-tokens $max_num_batched_tokens \
       --max-model-len $max_model_len \
       --no-enable-prefix-caching \
       --swap-space 16 \
       --disable-log-requests \
       --trust-remote-code \
       --gpu-memory-utilization 0.9
   

   Wait until the model has loaded and the server is ready to accept requests.

2. On another terminal on the same machine, run the benchmark:

   # Connect to the container
   docker exec -it test bash
   
   # Wait for the server to start
   until curl -s http://localhost:8000/v1/models; do sleep 30; done
   
   # Run the benchmark
   model=amd/Llama-3.1-8B-Instruct-FP8-KV
   max_concurrency=1
   num_prompts=10
   in=128
   out=128
   vllm bench serve --model $model \
       --percentile-metrics "ttft,tpot,itl,e2el" \
       --dataset-name random \
       --ignore-eos \
       --max-concurrency $max_concurrency \
       --num-prompts $num_prompts \
       --random-input-len $in \
       --random-output-len $out \
       --trust-remote-code \
       --save-result \
       --result-filename ${model}_serving.json
   

Note

If you encounter the following error, pass your access-authorized Hugging Face token to the gated models.

OSError: You are trying to access a gated repo.

# pass your HF_TOKEN
export HF_TOKEN=$your_personal_hf_token

Note

Throughput is calculated as:

• \[throughput\_tot = requests \times (\mathsf{\text{input lengths}} + \mathsf{\text{output lengths}}) / elapsed\_time\]
• \[throughput\_gen = requests \times \mathsf{\text{output lengths}} / elapsed\_time\]

MAD-integrated benchmarking

The following run command is tailored to Llama 3.1 70B FP8. See Supported models to switch to another available model.

1. Clone the ROCm Model Automation and Dashboarding (ROCm/MAD) repository to a local directory and install the required packages on the host machine.

   git clone https://github.com/ROCm/MAD
   cd MAD
   pip install -r requirements.txt
   

2. Use this command to run the performance benchmark test on the Llama 3.1 70B FP8 model using one GPU with the float8 data type on the host machine.

   export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
   madengine run \
       --tags pyt_vllm_llama-3.1-70b_fp8 \
       --keep-model-dir \
       --live-output \
       --timeout 28800
   

MAD launches a Docker container with the name container_ci-pyt_vllm_llama-3.1-70b_fp8. The throughput and serving reports of the model are collected in the following paths: pyt_vllm_llama-3.1-70b_fp8_throughput.csv and pyt_vllm_llama-3.1-70b_fp8_serving.csv.

Although the available models are preconfigured to collect offline throughput and online serving performance data, you can also change the benchmarking parameters. See the standalone benchmarking tab for more information.

Standalone benchmarking

The following commands are optimized for Llama 3.1 70B FP8. See Supported models to switch to another available model.

See also

For more information on configuration, see the config files in the MAD repository. Refer to the vLLM engine for descriptions of available configuration options and Benchmarking vLLM for additional benchmarking information.

Launch the container

You can run the vLLM benchmark tool independently by starting the Docker container as shown in the following snippet.

docker pull rocm/vllm:rocm6.4.1_vllm_0.10.1_20250909
docker run -it \
    --device=/dev/kfd \
    --device=/dev/dri \
    --group-add video \
    --shm-size 16G \
    --security-opt seccomp=unconfined \
    --security-opt apparmor=unconfined \
    --cap-add=SYS_PTRACE \
    -v $(pwd):/workspace \
    --env HUGGINGFACE_HUB_CACHE=/workspace \
    --name test \
    rocm/vllm:rocm6.4.1_vllm_0.10.1_20250909

Throughput command

Use the following command to start the throughput benchmark.

model=amd/Llama-3.1-70B-Instruct-FP8-KV
tp=8
num_prompts=1024
in=128
out=128
dtype=auto
kv_cache_dtype=fp8
max_num_seqs=1024
max_seq_len_to_capture=131072
max_num_batched_tokens=131072
max_model_len=8192

vllm bench throughput --model $model \
    -tp $tp \
    --num-prompts $num_prompts \
    --input-len $in \
    --output-len $out \
    --dtype $dtype \
    --kv-cache-dtype $kv_cache_dtype \
    --max-num-seqs $max_num_seqs \
    --max-seq-len-to-capture $max_seq_len_to_capture \
    --max-num-batched-tokens $max_num_batched_tokens \
    --max-model-len $max_model_len \
    --trust-remote-code \
    --output-json ${model}_throughput.json \
    --gpu-memory-utilization 0.9

Serving command

1. Start the server using the following command:

   model=amd/Llama-3.1-70B-Instruct-FP8-KV
   tp=8
   dtype=auto
   kv_cache_dtype=fp8
   max_num_seqs=256
   max_seq_len_to_capture=131072
   max_num_batched_tokens=131072
   max_model_len=8192
   
   vllm serve $model \
       -tp $tp \
       --dtype $dtype \
       --kv-cache-dtype $kv_cache_dtype \
       --max-num-seqs $max_num_seqs \
       --max-seq-len-to-capture $max_seq_len_to_capture \
       --max-num-batched-tokens $max_num_batched_tokens \
       --max-model-len $max_model_len \
       --no-enable-prefix-caching \
       --swap-space 16 \
       --disable-log-requests \
       --trust-remote-code \
       --gpu-memory-utilization 0.9
   

   Wait until the model has loaded and the server is ready to accept requests.

2. On another terminal on the same machine, run the benchmark:

   # Connect to the container
   docker exec -it test bash
   
   # Wait for the server to start
   until curl -s http://localhost:8000/v1/models; do sleep 30; done
   
   # Run the benchmark
   model=amd/Llama-3.1-70B-Instruct-FP8-KV
   max_concurrency=1
   num_prompts=10
   in=128
   out=128
   vllm bench serve --model $model \
       --percentile-metrics "ttft,tpot,itl,e2el" \
       --dataset-name random \
       --ignore-eos \
       --max-concurrency $max_concurrency \
       --num-prompts $num_prompts \
       --random-input-len $in \
       --random-output-len $out \
       --trust-remote-code \
       --save-result \
       --result-filename ${model}_serving.json
   

Note

If you encounter the following error, pass your access-authorized Hugging Face token to the gated models.

OSError: You are trying to access a gated repo.

# pass your HF_TOKEN
export HF_TOKEN=$your_personal_hf_token

Note

Throughput is calculated as:

• \[throughput\_tot = requests \times (\mathsf{\text{input lengths}} + \mathsf{\text{output lengths}}) / elapsed\_time\]
• \[throughput\_gen = requests \times \mathsf{\text{output lengths}} / elapsed\_time\]

MAD-integrated benchmarking

The following run command is tailored to Llama 3.1 405B FP8. See Supported models to switch to another available model.

1. Clone the ROCm Model Automation and Dashboarding (ROCm/MAD) repository to a local directory and install the required packages on the host machine.

   git clone https://github.com/ROCm/MAD
   cd MAD
   pip install -r requirements.txt
   

2. Use this command to run the performance benchmark test on the Llama 3.1 405B FP8 model using one GPU with the float8 data type on the host machine.

   export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
   madengine run \
       --tags pyt_vllm_llama-3.1-405b_fp8 \
       --keep-model-dir \
       --live-output \
       --timeout 28800
   

MAD launches a Docker container with the name container_ci-pyt_vllm_llama-3.1-405b_fp8. The throughput and serving reports of the model are collected in the following paths: pyt_vllm_llama-3.1-405b_fp8_throughput.csv and pyt_vllm_llama-3.1-405b_fp8_serving.csv.

Although the available models are preconfigured to collect offline throughput and online serving performance data, you can also change the benchmarking parameters. See the standalone benchmarking tab for more information.

Standalone benchmarking

The following commands are optimized for Llama 3.1 405B FP8. See Supported models to switch to another available model.

See also

For more information on configuration, see the config files in the MAD repository. Refer to the vLLM engine for descriptions of available configuration options and Benchmarking vLLM for additional benchmarking information.

Launch the container

You can run the vLLM benchmark tool independently by starting the Docker container as shown in the following snippet.

docker pull rocm/vllm:rocm6.4.1_vllm_0.10.1_20250909
docker run -it \
    --device=/dev/kfd \
    --device=/dev/dri \
    --group-add video \
    --shm-size 16G \
    --security-opt seccomp=unconfined \
    --security-opt apparmor=unconfined \
    --cap-add=SYS_PTRACE \
    -v $(pwd):/workspace \
    --env HUGGINGFACE_HUB_CACHE=/workspace \
    --name test \
    rocm/vllm:rocm6.4.1_vllm_0.10.1_20250909

Throughput command

Use the following command to start the throughput benchmark.

model=amd/Llama-3.1-405B-Instruct-FP8-KV
tp=8
num_prompts=1024
in=128
out=128
dtype=auto
kv_cache_dtype=fp8
max_num_seqs=1024
max_seq_len_to_capture=131072
max_num_batched_tokens=131072
max_model_len=8192

vllm bench throughput --model $model \
    -tp $tp \
    --num-prompts $num_prompts \
    --input-len $in \
    --output-len $out \
    --dtype $dtype \
    --kv-cache-dtype $kv_cache_dtype \
    --max-num-seqs $max_num_seqs \
    --max-seq-len-to-capture $max_seq_len_to_capture \
    --max-num-batched-tokens $max_num_batched_tokens \
    --max-model-len $max_model_len \
    --trust-remote-code \
    --output-json ${model}_throughput.json \
    --gpu-memory-utilization 0.9

Serving command

1. Start the server using the following command:

   model=amd/Llama-3.1-405B-Instruct-FP8-KV
   tp=8
   dtype=auto
   kv_cache_dtype=fp8
   max_num_seqs=256
   max_seq_len_to_capture=131072
   max_num_batched_tokens=131072
   max_model_len=8192
   
   vllm serve $model \
       -tp $tp \
       --dtype $dtype \
       --kv-cache-dtype $kv_cache_dtype \
       --max-num-seqs $max_num_seqs \
       --max-seq-len-to-capture $max_seq_len_to_capture \
       --max-num-batched-tokens $max_num_batched_tokens \
       --max-model-len $max_model_len \
       --no-enable-prefix-caching \
       --swap-space 16 \
       --disable-log-requests \
       --trust-remote-code \
       --gpu-memory-utilization 0.9
   

   Wait until the model has loaded and the server is ready to accept requests.

2. On another terminal on the same machine, run the benchmark:

   # Connect to the container
   docker exec -it test bash
   
   # Wait for the server to start
   until curl -s http://localhost:8000/v1/models; do sleep 30; done
   
   # Run the benchmark
   model=amd/Llama-3.1-405B-Instruct-FP8-KV
   max_concurrency=1
   num_prompts=10
   in=128
   out=128
   vllm bench serve --model $model \
       --percentile-metrics "ttft,tpot,itl,e2el" \
       --dataset-name random \
       --ignore-eos \
       --max-concurrency $max_concurrency \
       --num-prompts $num_prompts \
       --random-input-len $in \
       --random-output-len $out \
       --trust-remote-code \
       --save-result \
       --result-filename ${model}_serving.json
   

Note

If you encounter the following error, pass your access-authorized Hugging Face token to the gated models.

OSError: You are trying to access a gated repo.

# pass your HF_TOKEN
export HF_TOKEN=$your_personal_hf_token

Note

Throughput is calculated as:

• \[throughput\_tot = requests \times (\mathsf{\text{input lengths}} + \mathsf{\text{output lengths}}) / elapsed\_time\]
• \[throughput\_gen = requests \times \mathsf{\text{output lengths}} / elapsed\_time\]

MAD-integrated benchmarking

The following run command is tailored to Mixtral MoE 8x7B. See Supported models to switch to another available model.

1. Clone the ROCm Model Automation and Dashboarding (ROCm/MAD) repository to a local directory and install the required packages on the host machine.

   git clone https://github.com/ROCm/MAD
   cd MAD
   pip install -r requirements.txt
   

2. Use this command to run the performance benchmark test on the Mixtral MoE 8x7B model using one GPU with the float16 data type on the host machine.

   export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
   madengine run \
       --tags pyt_vllm_mixtral-8x7b \
       --keep-model-dir \
       --live-output \
       --timeout 28800
   

MAD launches a Docker container with the name container_ci-pyt_vllm_mixtral-8x7b. The throughput and serving reports of the model are collected in the following paths: pyt_vllm_mixtral-8x7b_throughput.csv and pyt_vllm_mixtral-8x7b_serving.csv.

Although the available models are preconfigured to collect offline throughput and online serving performance data, you can also change the benchmarking parameters. See the standalone benchmarking tab for more information.

Standalone benchmarking

The following commands are optimized for Mixtral MoE 8x7B. See Supported models to switch to another available model.

See also

For more information on configuration, see the config files in the MAD repository. Refer to the vLLM engine for descriptions of available configuration options and Benchmarking vLLM for additional benchmarking information.

Launch the container

You can run the vLLM benchmark tool independently by starting the Docker container as shown in the following snippet.

docker pull rocm/vllm:rocm6.4.1_vllm_0.10.1_20250909
docker run -it \
    --device=/dev/kfd \
    --device=/dev/dri \
    --group-add video \
    --shm-size 16G \
    --security-opt seccomp=unconfined \
    --security-opt apparmor=unconfined \
    --cap-add=SYS_PTRACE \
    -v $(pwd):/workspace \
    --env HUGGINGFACE_HUB_CACHE=/workspace \
    --name test \
    rocm/vllm:rocm6.4.1_vllm_0.10.1_20250909

Throughput command

Use the following command to start the throughput benchmark.

model=mistralai/Mixtral-8x7B-Instruct-v0.1
tp=8
num_prompts=1024
in=128
out=128
dtype=auto
kv_cache_dtype=auto
max_num_seqs=1024
max_seq_len_to_capture=32768
max_num_batched_tokens=32768
max_model_len=8192

vllm bench throughput --model $model \
    -tp $tp \
    --num-prompts $num_prompts \
    --input-len $in \
    --output-len $out \
    --dtype $dtype \
    --kv-cache-dtype $kv_cache_dtype \
    --max-num-seqs $max_num_seqs \
    --max-seq-len-to-capture $max_seq_len_to_capture \
    --max-num-batched-tokens $max_num_batched_tokens \
    --max-model-len $max_model_len \
    --trust-remote-code \
    --output-json ${model}_throughput.json \
    --gpu-memory-utilization 0.9

Serving command

1. Start the server using the following command:

   model=mistralai/Mixtral-8x7B-Instruct-v0.1
   tp=8
   dtype=auto
   kv_cache_dtype=auto
   max_num_seqs=256
   max_seq_len_to_capture=32768
   max_num_batched_tokens=32768
   max_model_len=8192
   
   vllm serve $model \
       -tp $tp \
       --dtype $dtype \
       --kv-cache-dtype $kv_cache_dtype \
       --max-num-seqs $max_num_seqs \
       --max-seq-len-to-capture $max_seq_len_to_capture \
       --max-num-batched-tokens $max_num_batched_tokens \
       --max-model-len $max_model_len \
       --no-enable-prefix-caching \
       --swap-space 16 \
       --disable-log-requests \
       --trust-remote-code \
       --gpu-memory-utilization 0.9
   

   Wait until the model has loaded and the server is ready to accept requests.

2. On another terminal on the same machine, run the benchmark:

   # Connect to the container
   docker exec -it test bash
   
   # Wait for the server to start
   until curl -s http://localhost:8000/v1/models; do sleep 30; done
   
   # Run the benchmark
   model=mistralai/Mixtral-8x7B-Instruct-v0.1
   max_concurrency=1
   num_prompts=10
   in=128
   out=128
   vllm bench serve --model $model \
       --percentile-metrics "ttft,tpot,itl,e2el" \
       --dataset-name random \
       --ignore-eos \
       --max-concurrency $max_concurrency \
       --num-prompts $num_prompts \
       --random-input-len $in \
       --random-output-len $out \
       --trust-remote-code \
       --save-result \
       --result-filename ${model}_serving.json
   

Note

If you encounter the following error, pass your access-authorized Hugging Face token to the gated models.

OSError: You are trying to access a gated repo.

# pass your HF_TOKEN
export HF_TOKEN=$your_personal_hf_token

Note

Throughput is calculated as:

• \[throughput\_tot = requests \times (\mathsf{\text{input lengths}} + \mathsf{\text{output lengths}}) / elapsed\_time\]
• \[throughput\_gen = requests \times \mathsf{\text{output lengths}} / elapsed\_time\]

MAD-integrated benchmarking

The following run command is tailored to Mixtral MoE 8x22B. See Supported models to switch to another available model.

1. Clone the ROCm Model Automation and Dashboarding (ROCm/MAD) repository to a local directory and install the required packages on the host machine.

   git clone https://github.com/ROCm/MAD
   cd MAD
   pip install -r requirements.txt
   

2. Use this command to run the performance benchmark test on the Mixtral MoE 8x22B model using one GPU with the float16 data type on the host machine.

   export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
   madengine run \
       --tags pyt_vllm_mixtral-8x22b \
       --keep-model-dir \
       --live-output \
       --timeout 28800
   

MAD launches a Docker container with the name container_ci-pyt_vllm_mixtral-8x22b. The throughput and serving reports of the model are collected in the following paths: pyt_vllm_mixtral-8x22b_throughput.csv and pyt_vllm_mixtral-8x22b_serving.csv.

Although the available models are preconfigured to collect offline throughput and online serving performance data, you can also change the benchmarking parameters. See the standalone benchmarking tab for more information.

Standalone benchmarking

The following commands are optimized for Mixtral MoE 8x22B. See Supported models to switch to another available model.

See also

For more information on configuration, see the config files in the MAD repository. Refer to the vLLM engine for descriptions of available configuration options and Benchmarking vLLM for additional benchmarking information.

Launch the container

You can run the vLLM benchmark tool independently by starting the Docker container as shown in the following snippet.

docker pull rocm/vllm:rocm6.4.1_vllm_0.10.1_20250909
docker run -it \
    --device=/dev/kfd \
    --device=/dev/dri \
    --group-add video \
    --shm-size 16G \
    --security-opt seccomp=unconfined \
    --security-opt apparmor=unconfined \
    --cap-add=SYS_PTRACE \
    -v $(pwd):/workspace \
    --env HUGGINGFACE_HUB_CACHE=/workspace \
    --name test \
    rocm/vllm:rocm6.4.1_vllm_0.10.1_20250909

Throughput command

Use the following command to start the throughput benchmark.

model=mistralai/Mixtral-8x22B-Instruct-v0.1
tp=8
num_prompts=1024
in=128
out=128
dtype=auto
kv_cache_dtype=auto
max_num_seqs=1024
max_seq_len_to_capture=65536
max_num_batched_tokens=65536
max_model_len=8192

vllm bench throughput --model $model \
    -tp $tp \
    --num-prompts $num_prompts \
    --input-len $in \
    --output-len $out \
    --dtype $dtype \
    --kv-cache-dtype $kv_cache_dtype \
    --max-num-seqs $max_num_seqs \
    --max-seq-len-to-capture $max_seq_len_to_capture \
    --max-num-batched-tokens $max_num_batched_tokens \
    --max-model-len $max_model_len \
    --trust-remote-code \
    --output-json ${model}_throughput.json \
    --gpu-memory-utilization 0.9

Serving command

1. Start the server using the following command:

   model=mistralai/Mixtral-8x22B-Instruct-v0.1
   tp=8
   dtype=auto
   kv_cache_dtype=auto
   max_num_seqs=256
   max_seq_len_to_capture=65536
   max_num_batched_tokens=65536
   max_model_len=8192
   
   vllm serve $model \
       -tp $tp \
       --dtype $dtype \
       --kv-cache-dtype $kv_cache_dtype \
       --max-num-seqs $max_num_seqs \
       --max-seq-len-to-capture $max_seq_len_to_capture \
       --max-num-batched-tokens $max_num_batched_tokens \
       --max-model-len $max_model_len \
       --no-enable-prefix-caching \
       --swap-space 16 \
       --disable-log-requests \
       --trust-remote-code \
       --gpu-memory-utilization 0.9
   

   Wait until the model has loaded and the server is ready to accept requests.

2. On another terminal on the same machine, run the benchmark:

   # Connect to the container
   docker exec -it test bash
   
   # Wait for the server to start
   until curl -s http://localhost:8000/v1/models; do sleep 30; done
   
   # Run the benchmark
   model=mistralai/Mixtral-8x22B-Instruct-v0.1
   max_concurrency=1
   num_prompts=10
   in=128
   out=128
   vllm bench serve --model $model \
       --percentile-metrics "ttft,tpot,itl,e2el" \
       --dataset-name random \
       --ignore-eos \
       --max-concurrency $max_concurrency \
       --num-prompts $num_prompts \
       --random-input-len $in \
       --random-output-len $out \
       --trust-remote-code \
       --save-result \
       --result-filename ${model}_serving.json
   

Note

If you encounter the following error, pass your access-authorized Hugging Face token to the gated models.

OSError: You are trying to access a gated repo.

# pass your HF_TOKEN
export HF_TOKEN=$your_personal_hf_token

Note

Throughput is calculated as:

• \[throughput\_tot = requests \times (\mathsf{\text{input lengths}} + \mathsf{\text{output lengths}}) / elapsed\_time\]
• \[throughput\_gen = requests \times \mathsf{\text{output lengths}} / elapsed\_time\]

MAD-integrated benchmarking

The following run command is tailored to Mixtral MoE 8x7B FP8. See Supported models to switch to another available model.

1. Clone the ROCm Model Automation and Dashboarding (ROCm/MAD) repository to a local directory and install the required packages on the host machine.

   git clone https://github.com/ROCm/MAD
   cd MAD
   pip install -r requirements.txt
   

2. Use this command to run the performance benchmark test on the Mixtral MoE 8x7B FP8 model using one GPU with the float8 data type on the host machine.

   export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
   madengine run \
       --tags pyt_vllm_mixtral-8x7b_fp8 \
       --keep-model-dir \
       --live-output \
       --timeout 28800
   

MAD launches a Docker container with the name container_ci-pyt_vllm_mixtral-8x7b_fp8. The throughput and serving reports of the model are collected in the following paths: pyt_vllm_mixtral-8x7b_fp8_throughput.csv and pyt_vllm_mixtral-8x7b_fp8_serving.csv.

Although the available models are preconfigured to collect offline throughput and online serving performance data, you can also change the benchmarking parameters. See the standalone benchmarking tab for more information.

Standalone benchmarking

The following commands are optimized for Mixtral MoE 8x7B FP8. See Supported models to switch to another available model.

See also

For more information on configuration, see the config files in the MAD repository. Refer to the vLLM engine for descriptions of available configuration options and Benchmarking vLLM for additional benchmarking information.

Launch the container

You can run the vLLM benchmark tool independently by starting the Docker container as shown in the following snippet.

docker pull rocm/vllm:rocm6.4.1_vllm_0.10.1_20250909
docker run -it \
    --device=/dev/kfd \
    --device=/dev/dri \
    --group-add video \
    --shm-size 16G \
    --security-opt seccomp=unconfined \
    --security-opt apparmor=unconfined \
    --cap-add=SYS_PTRACE \
    -v $(pwd):/workspace \
    --env HUGGINGFACE_HUB_CACHE=/workspace \
    --name test \
    rocm/vllm:rocm6.4.1_vllm_0.10.1_20250909

Throughput command

Use the following command to start the throughput benchmark.

model=amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV
tp=8
num_prompts=1024
in=128
out=128
dtype=auto
kv_cache_dtype=fp8
max_num_seqs=1024
max_seq_len_to_capture=32768
max_num_batched_tokens=32768
max_model_len=8192

vllm bench throughput --model $model \
    -tp $tp \
    --num-prompts $num_prompts \
    --input-len $in \
    --output-len $out \
    --dtype $dtype \
    --kv-cache-dtype $kv_cache_dtype \
    --max-num-seqs $max_num_seqs \
    --max-seq-len-to-capture $max_seq_len_to_capture \
    --max-num-batched-tokens $max_num_batched_tokens \
    --max-model-len $max_model_len \
    --trust-remote-code \
    --output-json ${model}_throughput.json \
    --gpu-memory-utilization 0.9

Serving command

1. Start the server using the following command:

   model=amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV
   tp=8
   dtype=auto
   kv_cache_dtype=fp8
   max_num_seqs=256
   max_seq_len_to_capture=32768
   max_num_batched_tokens=32768
   max_model_len=8192
   
   vllm serve $model \
       -tp $tp \
       --dtype $dtype \
       --kv-cache-dtype $kv_cache_dtype \
       --max-num-seqs $max_num_seqs \
       --max-seq-len-to-capture $max_seq_len_to_capture \
       --max-num-batched-tokens $max_num_batched_tokens \
       --max-model-len $max_model_len \
       --no-enable-prefix-caching \
       --swap-space 16 \
       --disable-log-requests \
       --trust-remote-code \
       --gpu-memory-utilization 0.9
   

   Wait until the model has loaded and the server is ready to accept requests.

2. On another terminal on the same machine, run the benchmark:

   # Connect to the container
   docker exec -it test bash
   
   # Wait for the server to start
   until curl -s http://localhost:8000/v1/models; do sleep 30; done
   
   # Run the benchmark
   model=amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV
   max_concurrency=1
   num_prompts=10
   in=128
   out=128
   vllm bench serve --model $model \
       --percentile-metrics "ttft,tpot,itl,e2el" \
       --dataset-name random \
       --ignore-eos \
       --max-concurrency $max_concurrency \
       --num-prompts $num_prompts \
       --random-input-len $in \
       --random-output-len $out \
       --trust-remote-code \
       --save-result \
       --result-filename ${model}_serving.json
   

Note

If you encounter the following error, pass your access-authorized Hugging Face token to the gated models.

OSError: You are trying to access a gated repo.

# pass your HF_TOKEN
export HF_TOKEN=$your_personal_hf_token

Note

Throughput is calculated as:

• \[throughput\_tot = requests \times (\mathsf{\text{input lengths}} + \mathsf{\text{output lengths}}) / elapsed\_time\]
• \[throughput\_gen = requests \times \mathsf{\text{output lengths}} / elapsed\_time\]

MAD-integrated benchmarking

The following run command is tailored to Mixtral MoE 8x22B FP8. See Supported models to switch to another available model.

1. Clone the ROCm Model Automation and Dashboarding (ROCm/MAD) repository to a local directory and install the required packages on the host machine.

   git clone https://github.com/ROCm/MAD
   cd MAD
   pip install -r requirements.txt
   

2. Use this command to run the performance benchmark test on the Mixtral MoE 8x22B FP8 model using one GPU with the float8 data type on the host machine.

   export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
   madengine run \
       --tags pyt_vllm_mixtral-8x22b_fp8 \
       --keep-model-dir \
       --live-output \
       --timeout 28800
   

MAD launches a Docker container with the name container_ci-pyt_vllm_mixtral-8x22b_fp8. The throughput and serving reports of the model are collected in the following paths: pyt_vllm_mixtral-8x22b_fp8_throughput.csv and pyt_vllm_mixtral-8x22b_fp8_serving.csv.

Although the available models are preconfigured to collect offline throughput and online serving performance data, you can also change the benchmarking parameters. See the standalone benchmarking tab for more information.

Standalone benchmarking

The following commands are optimized for Mixtral MoE 8x22B FP8. See Supported models to switch to another available model.

See also

For more information on configuration, see the config files in the MAD repository. Refer to the vLLM engine for descriptions of available configuration options and Benchmarking vLLM for additional benchmarking information.

Launch the container

You can run the vLLM benchmark tool independently by starting the Docker container as shown in the following snippet.

docker pull rocm/vllm:rocm6.4.1_vllm_0.10.1_20250909
docker run -it \
    --device=/dev/kfd \
    --device=/dev/dri \
    --group-add video \
    --shm-size 16G \
    --security-opt seccomp=unconfined \
    --security-opt apparmor=unconfined \
    --cap-add=SYS_PTRACE \
    -v $(pwd):/workspace \
    --env HUGGINGFACE_HUB_CACHE=/workspace \
    --name test \
    rocm/vllm:rocm6.4.1_vllm_0.10.1_20250909

Throughput command

Use the following command to start the throughput benchmark.

model=amd/Mixtral-8x22B-Instruct-v0.1-FP8-KV
tp=8
num_prompts=1024
in=128
out=128
dtype=auto
kv_cache_dtype=fp8
max_num_seqs=1024
max_seq_len_to_capture=65536
max_num_batched_tokens=65536
max_model_len=8192

vllm bench throughput --model $model \
    -tp $tp \
    --num-prompts $num_prompts \
    --input-len $in \
    --output-len $out \
    --dtype $dtype \
    --kv-cache-dtype $kv_cache_dtype \
    --max-num-seqs $max_num_seqs \
    --max-seq-len-to-capture $max_seq_len_to_capture \
    --max-num-batched-tokens $max_num_batched_tokens \
    --max-model-len $max_model_len \
    --trust-remote-code \
    --output-json ${model}_throughput.json \
    --gpu-memory-utilization 0.9

Serving command

1. Start the server using the following command:

   model=amd/Mixtral-8x22B-Instruct-v0.1-FP8-KV
   tp=8
   dtype=auto
   kv_cache_dtype=fp8
   max_num_seqs=256
   max_seq_len_to_capture=65536
   max_num_batched_tokens=65536
   max_model_len=8192
   
   vllm serve $model \
       -tp $tp \
       --dtype $dtype \
       --kv-cache-dtype $kv_cache_dtype \
       --max-num-seqs $max_num_seqs \
       --max-seq-len-to-capture $max_seq_len_to_capture \
       --max-num-batched-tokens $max_num_batched_tokens \
       --max-model-len $max_model_len \
       --no-enable-prefix-caching \
       --swap-space 16 \
       --disable-log-requests \
       --trust-remote-code \
       --gpu-memory-utilization 0.9
   

   Wait until the model has loaded and the server is ready to accept requests.

2. On another terminal on the same machine, run the benchmark:

   # Connect to the container
   docker exec -it test bash
   
   # Wait for the server to start
   until curl -s http://localhost:8000/v1/models; do sleep 30; done
   
   # Run the benchmark
   model=amd/Mixtral-8x22B-Instruct-v0.1-FP8-KV
   max_concurrency=1
   num_prompts=10
   in=128
   out=128
   vllm bench serve --model $model \
       --percentile-metrics "ttft,tpot,itl,e2el" \
       --dataset-name random \
       --ignore-eos \
       --max-concurrency $max_concurrency \
       --num-prompts $num_prompts \
       --random-input-len $in \
       --random-output-len $out \
       --trust-remote-code \
       --save-result \
       --result-filename ${model}_serving.json
   

Note

If you encounter the following error, pass your access-authorized Hugging Face token to the gated models.

OSError: You are trying to access a gated repo.

# pass your HF_TOKEN
export HF_TOKEN=$your_personal_hf_token

Note

Throughput is calculated as:

• \[throughput\_tot = requests \times (\mathsf{\text{input lengths}} + \mathsf{\text{output lengths}}) / elapsed\_time\]
• \[throughput\_gen = requests \times \mathsf{\text{output lengths}} / elapsed\_time\]

MAD-integrated benchmarking

The following run command is tailored to QwQ-32B. See Supported models to switch to another available model.

1. Clone the ROCm Model Automation and Dashboarding (ROCm/MAD) repository to a local directory and install the required packages on the host machine.

   git clone https://github.com/ROCm/MAD
   cd MAD
   pip install -r requirements.txt
   

2. Use this command to run the performance benchmark test on the QwQ-32B model using one GPU with the float16 data type on the host machine.

   export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
   madengine run \
       --tags pyt_vllm_qwq-32b \
       --keep-model-dir \
       --live-output \
       --timeout 28800
   

MAD launches a Docker container with the name container_ci-pyt_vllm_qwq-32b. The throughput and serving reports of the model are collected in the following paths: pyt_vllm_qwq-32b_throughput.csv and pyt_vllm_qwq-32b_serving.csv.

Although the available models are preconfigured to collect offline throughput and online serving performance data, you can also change the benchmarking parameters. See the standalone benchmarking tab for more information.

Standalone benchmarking

The following commands are optimized for QwQ-32B. See Supported models to switch to another available model.

See also

For more information on configuration, see the config files in the MAD repository. Refer to the vLLM engine for descriptions of available configuration options and Benchmarking vLLM for additional benchmarking information.

Launch the container

You can run the vLLM benchmark tool independently by starting the Docker container as shown in the following snippet.

docker pull rocm/vllm:rocm6.4.1_vllm_0.10.1_20250909
docker run -it \
    --device=/dev/kfd \
    --device=/dev/dri \
    --group-add video \
    --shm-size 16G \
    --security-opt seccomp=unconfined \
    --security-opt apparmor=unconfined \
    --cap-add=SYS_PTRACE \
    -v $(pwd):/workspace \
    --env HUGGINGFACE_HUB_CACHE=/workspace \
    --name test \
    rocm/vllm:rocm6.4.1_vllm_0.10.1_20250909

Throughput command

Use the following command to start the throughput benchmark.

model=Qwen/QwQ-32B
tp=1
num_prompts=1024
in=128
out=128
dtype=auto
kv_cache_dtype=auto
max_num_seqs=1024
max_seq_len_to_capture=131072
max_num_batched_tokens=131072
max_model_len=8192

vllm bench throughput --model $model \
    -tp $tp \
    --num-prompts $num_prompts \
    --input-len $in \
    --output-len $out \
    --dtype $dtype \
    --kv-cache-dtype $kv_cache_dtype \
    --max-num-seqs $max_num_seqs \
    --max-seq-len-to-capture $max_seq_len_to_capture \
    --max-num-batched-tokens $max_num_batched_tokens \
    --max-model-len $max_model_len \
    --trust-remote-code \
    --output-json ${model}_throughput.json \
    --gpu-memory-utilization 0.9

Serving command

1. Start the server using the following command:

   model=Qwen/QwQ-32B
   tp=1
   dtype=auto
   kv_cache_dtype=auto
   max_num_seqs=256
   max_seq_len_to_capture=131072
   max_num_batched_tokens=131072
   max_model_len=8192
   
   vllm serve $model \
       -tp $tp \
       --dtype $dtype \
       --kv-cache-dtype $kv_cache_dtype \
       --max-num-seqs $max_num_seqs \
       --max-seq-len-to-capture $max_seq_len_to_capture \
       --max-num-batched-tokens $max_num_batched_tokens \
       --max-model-len $max_model_len \
       --no-enable-prefix-caching \
       --swap-space 16 \
       --disable-log-requests \
       --trust-remote-code \
       --gpu-memory-utilization 0.9
   

   Wait until the model has loaded and the server is ready to accept requests.

2. On another terminal on the same machine, run the benchmark:

   # Connect to the container
   docker exec -it test bash
   
   # Wait for the server to start
   until curl -s http://localhost:8000/v1/models; do sleep 30; done
   
   # Run the benchmark
   model=Qwen/QwQ-32B
   max_concurrency=1
   num_prompts=10
   in=128
   out=128
   vllm bench serve --model $model \
       --percentile-metrics "ttft,tpot,itl,e2el" \
       --dataset-name random \
       --ignore-eos \
       --max-concurrency $max_concurrency \
       --num-prompts $num_prompts \
       --random-input-len $in \
       --random-output-len $out \
       --trust-remote-code \
       --save-result \
       --result-filename ${model}_serving.json
   

Note

If you encounter the following error, pass your access-authorized Hugging Face token to the gated models.

OSError: You are trying to access a gated repo.

# pass your HF_TOKEN
export HF_TOKEN=$your_personal_hf_token

Note

Throughput is calculated as:

• \[throughput\_tot = requests \times (\mathsf{\text{input lengths}} + \mathsf{\text{output lengths}}) / elapsed\_time\]
• \[throughput\_gen = requests \times \mathsf{\text{output lengths}} / elapsed\_time\]

MAD-integrated benchmarking

The following run command is tailored to Qwen3 30B A3B. See Supported models to switch to another available model.

1. Clone the ROCm Model Automation and Dashboarding (ROCm/MAD) repository to a local directory and install the required packages on the host machine.

   git clone https://github.com/ROCm/MAD
   cd MAD
   pip install -r requirements.txt
   

2. Use this command to run the performance benchmark test on the Qwen3 30B A3B model using one GPU with the float16 data type on the host machine.

   export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
   madengine run \
       --tags pyt_vllm_qwen3-30b-a3b \
       --keep-model-dir \
       --live-output \
       --timeout 28800
   

MAD launches a Docker container with the name container_ci-pyt_vllm_qwen3-30b-a3b. The throughput and serving reports of the model are collected in the following paths: pyt_vllm_qwen3-30b-a3b_throughput.csv and pyt_vllm_qwen3-30b-a3b_serving.csv.

Although the available models are preconfigured to collect offline throughput and online serving performance data, you can also change the benchmarking parameters. See the standalone benchmarking tab for more information.

Standalone benchmarking

The following commands are optimized for Qwen3 30B A3B. See Supported models to switch to another available model.

See also

For more information on configuration, see the config files in the MAD repository. Refer to the vLLM engine for descriptions of available configuration options and Benchmarking vLLM for additional benchmarking information.

Launch the container

You can run the vLLM benchmark tool independently by starting the Docker container as shown in the following snippet.

docker pull rocm/vllm:rocm6.4.1_vllm_0.10.1_20250909
docker run -it \
    --device=/dev/kfd \
    --device=/dev/dri \
    --group-add video \
    --shm-size 16G \
    --security-opt seccomp=unconfined \
    --security-opt apparmor=unconfined \
    --cap-add=SYS_PTRACE \
    -v $(pwd):/workspace \
    --env HUGGINGFACE_HUB_CACHE=/workspace \
    --name test \
    rocm/vllm:rocm6.4.1_vllm_0.10.1_20250909

Throughput command

Use the following command to start the throughput benchmark.

model=Qwen/Qwen3-30B-A3B
tp=1
num_prompts=1024
in=128
out=128
dtype=auto
kv_cache_dtype=auto
max_num_seqs=1024
max_seq_len_to_capture=32768
max_num_batched_tokens=32768
max_model_len=8192

vllm bench throughput --model $model \
    -tp $tp \
    --num-prompts $num_prompts \
    --input-len $in \
    --output-len $out \
    --dtype $dtype \
    --kv-cache-dtype $kv_cache_dtype \
    --max-num-seqs $max_num_seqs \
    --max-seq-len-to-capture $max_seq_len_to_capture \
    --max-num-batched-tokens $max_num_batched_tokens \
    --max-model-len $max_model_len \
    --trust-remote-code \
    --output-json ${model}_throughput.json \
    --gpu-memory-utilization 0.9

Serving command

1. Start the server using the following command:

   model=Qwen/Qwen3-30B-A3B
   tp=1
   dtype=auto
   kv_cache_dtype=auto
   max_num_seqs=256
   max_seq_len_to_capture=32768
   max_num_batched_tokens=32768
   max_model_len=8192
   
   vllm serve $model \
       -tp $tp \
       --dtype $dtype \
       --kv-cache-dtype $kv_cache_dtype \
       --max-num-seqs $max_num_seqs \
       --max-seq-len-to-capture $max_seq_len_to_capture \
       --max-num-batched-tokens $max_num_batched_tokens \
       --max-model-len $max_model_len \
       --no-enable-prefix-caching \
       --swap-space 16 \
       --disable-log-requests \
       --trust-remote-code \
       --gpu-memory-utilization 0.9
   

   Wait until the model has loaded and the server is ready to accept requests.

2. On another terminal on the same machine, run the benchmark:

   # Connect to the container
   docker exec -it test bash
   
   # Wait for the server to start
   until curl -s http://localhost:8000/v1/models; do sleep 30; done
   
   # Run the benchmark
   model=Qwen/Qwen3-30B-A3B
   max_concurrency=1
   num_prompts=10
   in=128
   out=128
   vllm bench serve --model $model \
       --percentile-metrics "ttft,tpot,itl,e2el" \
       --dataset-name random \
       --ignore-eos \
       --max-concurrency $max_concurrency \
       --num-prompts $num_prompts \
       --random-input-len $in \
       --random-output-len $out \
       --trust-remote-code \
       --save-result \
       --result-filename ${model}_serving.json
   

Note

If you encounter the following error, pass your access-authorized Hugging Face token to the gated models.

OSError: You are trying to access a gated repo.

# pass your HF_TOKEN
export HF_TOKEN=$your_personal_hf_token

Note

Throughput is calculated as:

• \[throughput\_tot = requests \times (\mathsf{\text{input lengths}} + \mathsf{\text{output lengths}}) / elapsed\_time\]
• \[throughput\_gen = requests \times \mathsf{\text{output lengths}} / elapsed\_time\]

MAD-integrated benchmarking

The following run command is tailored to Phi-4. See Supported models to switch to another available model.

1. Clone the ROCm Model Automation and Dashboarding (ROCm/MAD) repository to a local directory and install the required packages on the host machine.

   git clone https://github.com/ROCm/MAD
   cd MAD
   pip install -r requirements.txt
   

2. Use this command to run the performance benchmark test on the Phi-4 model using one GPU with the :literal:`` data type on the host machine.

   export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
   madengine run \
       --tags pyt_vllm_phi-4 \
       --keep-model-dir \
       --live-output \
       --timeout 28800
   

MAD launches a Docker container with the name container_ci-pyt_vllm_phi-4. The throughput and serving reports of the model are collected in the following paths: pyt_vllm_phi-4_throughput.csv and pyt_vllm_phi-4_serving.csv.

Although the available models are preconfigured to collect offline throughput and online serving performance data, you can also change the benchmarking parameters. See the standalone benchmarking tab for more information.

Standalone benchmarking

The following commands are optimized for Phi-4. See Supported models to switch to another available model.

See also

For more information on configuration, see the config files in the MAD repository. Refer to the vLLM engine for descriptions of available configuration options and Benchmarking vLLM for additional benchmarking information.

Launch the container

You can run the vLLM benchmark tool independently by starting the Docker container as shown in the following snippet.

docker pull rocm/vllm:rocm6.4.1_vllm_0.10.1_20250909
docker run -it \
    --device=/dev/kfd \
    --device=/dev/dri \
    --group-add video \
    --shm-size 16G \
    --security-opt seccomp=unconfined \
    --security-opt apparmor=unconfined \
    --cap-add=SYS_PTRACE \
    -v $(pwd):/workspace \
    --env HUGGINGFACE_HUB_CACHE=/workspace \
    --name test \
    rocm/vllm:rocm6.4.1_vllm_0.10.1_20250909

Throughput command

Use the following command to start the throughput benchmark.

model=microsoft/phi-4
tp=1
num_prompts=1024
in=128
out=128
dtype=auto
kv_cache_dtype=auto
max_num_seqs=1024
max_seq_len_to_capture=16384
max_num_batched_tokens=16384
max_model_len=8192

vllm bench throughput --model $model \
    -tp $tp \
    --num-prompts $num_prompts \
    --input-len $in \
    --output-len $out \
    --dtype $dtype \
    --kv-cache-dtype $kv_cache_dtype \
    --max-num-seqs $max_num_seqs \
    --max-seq-len-to-capture $max_seq_len_to_capture \
    --max-num-batched-tokens $max_num_batched_tokens \
    --max-model-len $max_model_len \
    --trust-remote-code \
    --output-json ${model}_throughput.json \
    --gpu-memory-utilization 0.9

Serving command

1. Start the server using the following command:

   model=microsoft/phi-4
   tp=1
   dtype=auto
   kv_cache_dtype=auto
   max_num_seqs=256
   max_seq_len_to_capture=16384
   max_num_batched_tokens=16384
   max_model_len=8192
   
   vllm serve $model \
       -tp $tp \
       --dtype $dtype \
       --kv-cache-dtype $kv_cache_dtype \
       --max-num-seqs $max_num_seqs \
       --max-seq-len-to-capture $max_seq_len_to_capture \
       --max-num-batched-tokens $max_num_batched_tokens \
       --max-model-len $max_model_len \
       --no-enable-prefix-caching \
       --swap-space 16 \
       --disable-log-requests \
       --trust-remote-code \
       --gpu-memory-utilization 0.9
   

   Wait until the model has loaded and the server is ready to accept requests.

2. On another terminal on the same machine, run the benchmark:

   # Connect to the container
   docker exec -it test bash
   
   # Wait for the server to start
   until curl -s http://localhost:8000/v1/models; do sleep 30; done
   
   # Run the benchmark
   model=microsoft/phi-4
   max_concurrency=1
   num_prompts=10
   in=128
   out=128
   vllm bench serve --model $model \
       --percentile-metrics "ttft,tpot,itl,e2el" \
       --dataset-name random \
       --ignore-eos \
       --max-concurrency $max_concurrency \
       --num-prompts $num_prompts \
       --random-input-len $in \
       --random-output-len $out \
       --trust-remote-code \
       --save-result \
       --result-filename ${model}_serving.json
   

Note

If you encounter the following error, pass your access-authorized Hugging Face token to the gated models.

OSError: You are trying to access a gated repo.

# pass your HF_TOKEN
export HF_TOKEN=$your_personal_hf_token

Note

Throughput is calculated as:

• \[throughput\_tot = requests \times (\mathsf{\text{input lengths}} + \mathsf{\text{output lengths}}) / elapsed\_time\]
• \[throughput\_gen = requests \times \mathsf{\text{output lengths}} / elapsed\_time\]

Advanced usage

For information on experimental features and known issues related to ROCm optimization efforts on vLLM, see the developer’s guide at ROCm/vllm.

Reproducing the Docker image

To reproduce this ROCm/vLLM Docker image release, follow these steps:

1. Clone the vLLM repository.

   git clone https://github.com/ROCm/vllm.git
   

2. Checkout the specific release commit.

   cd vllm
   git checkout 6663000a391911eba96d7864a26ac42b07f6ef29
   

3. Build the Docker image. Replace vllm-rocm with your desired image tag.

   docker build -f docker/Dockerfile.rocm -t vllm-rocm .
   

Further reading

• To learn more about the options for latency and throughput benchmark scripts, see ROCm/vllm.

• To learn more about MAD and the madengine CLI, see the MAD usage guide.

• To learn more about system settings and management practices to configure your system for AMD Instinct MI300X series accelerators, see AMD Instinct MI300X system optimization.

• See vLLM inference and vLLM performance optimization for a brief introduction to vLLM and optimization strategies.

• For application performance optimization strategies for HPC and AI workloads, including inference with vLLM, see AMD Instinct MI300X workload optimization.

• For a list of other ready-made Docker images for AI with ROCm, see AMD Infinity Hub.

Previous versions

See vLLM inference performance testing version history to find documentation for previous releases of the ROCm/vllm Docker image.