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What is ROCm?
Release notes
Compatibility matrix
Linux system requirements
Windows system requirements
Install
ROCm on Linux
HIP SDK on Windows
ROCm on Radeon GPUs
Deep learning frameworks
Build ROCm from source
How to
Use ROCm for AI
Installation
Train a model
Scale model training
Run models from Hugging Face
Deploy your model
Use ROCm for HPC
Fine-tune LLMs and inference optimization
Conceptual overview
Fine-tuning and inference
Use a single accelerator
Use multiple accelerators
Model quantization techniques
Model acceleration libraries
LLM inference frameworks
Optimize with Composable Kernel
Optimize Triton kernels
Profile and debug
System optimization
AMD Instinct MI300X
AMD Instinct MI300A
AMD Instinct MI200
AMD Instinct MI100
AMD RDNA 2
AMD MI300X performance validation and tuning
Performance validation
System tuning
Workload tuning
GPU cluster networking
Use MPI
System debugging
Use advanced compiler features
ROCm compiler infrastructure
Use AddressSanitizer
OpenMP support
Set the number of CUs
ROCm examples
Conceptual
GPU architecture overview
MI300 microarchitecture
AMD Instinct MI300/CDNA3 ISA
White paper
MI300 and MI200 Performance counter
MI250 microarchitecture
AMD Instinct MI200/CDNA2 ISA
White paper
MI100 microarchitecture
AMD Instinct MI100/CDNA1 ISA
White paper
Input-Output Memory Management Unit (IOMMU)
File structure (Linux FHS)
GPU isolation techniques
Using CMake
PCIe atomics in ROCm
Inception v3 with PyTorch
Oversubscription of hardware resources
Reference
ROCm libraries
ROCm tools, compilers, and runtimes
Accelerator and GPU hardware specifications
Precision support
Contribute
Contributing to the ROCm docmentation
ROCm documentation toolchain
Providing feedback about the ROCm documentation
ROCm licenses
Index