Primus technical blogs

Primus technical blogs#

Every published article about Primus, newest first. The last column points at the maintained documentation or code for the same topic: a blog is frozen at its publication date, so where the two disagree, the documentation wins. The top-level README lists only the most recent handful.

Published#

Date

Title

What it covers

In this repo

2026/09/03

Enabling DeepSeek-V4-Flash Training on AMD Instinct MI355X GPUs with Primus

DeepSeek-V4-Flash architecture, performance projection, kernel optimizations, and how to reproduce the runs

examples/deepseek-v4

2026/08/12

Using ODC to Accelerate AMD SFT Training

On-demand point-to-point communication replacing the FSDP all-gather / reduce-scatter, over rocSHMEM and MORI

ODC FSDP2 patches

2026/07/06

Primus Tuning Agent: Closing the Configuration-Search Loop

LLM-driven search over parallelism, pipeline layout, and recompute sets, scored by projection instead of cluster time

Tuning agent

2026/06/10

Dropless MoE Training in JAX with Primus-Turbo

Grouped GEMM brought into MaxText through JAX FFI and custom_vjp, with the fan-out / fan-in correctness details

MaxText parameters

2026/04/24

Primus Projection: Estimate Memory and Performance Before You Train

Analytical memory estimation and benchmark-anchored throughput projection for multi-node runs

Projection

2026/02/23

Primus-Pipeline: A More Flexible and Scalable Pipeline Parallelism Implementation

Zero-bubble schedules (zerobubble / zbv / v-half / v-min) alongside 1F1B and interleaved, in the Megatron-LM backend

Parallelism strategies

2026/01/15

Deep Dive into Primus: High-Performance Training for Large Language Models

How the unified CLI and tuned backend presets reach peak dense-LLM throughput with minimal manual tuning

Megatron-LM training

2025/12/16

MoE Training Best Practices on AMD GPUs

The foundational MoE optimizations: DeepEP dispatch, sync-free MoE, 1F1B all-to-all overlap, selective recompute

MoE training deep-dive

2025/08/22

Primus: A Lightweight, Unified Training Framework for Large Models on AMD GPUs

The framework itself: YAML-driven configuration, multi-backend design, preflight validation, structured logging

Project overview

Awaiting publication#

Date

Title

What it covers

In this repo

2026/08/21

MoE Training Optimization with Primus

MegaMoE megakernel, FP8 / MXFP8 grouped GEMM, training-at-scale tuning

MegaMoE fused MoE layer

Adding an article#

Article sources are maintained in the ROCm Blogs repository, not here. Copies were vendored into this repository once before and drifted out of sync with the published versions, down to stale author lists, so this directory holds the index only. When an article goes live, add a row above and link to rocm.blogs.amd.com rather than adding a copy. Add it to the top-level README too if it belongs in the recent handful, dropping the oldest entry there to keep that list short.