Primus overview

July 21, 2026 · View on GitHub

Executive summary

Primus is a YAML-driven training framework for large-scale foundation model work on AMD GPUs. It targets machine learning engineers, researchers, and platform/operations teams who need reproducible, multi-backend training pipelines on AMD Instinct™ hardware. This project, also referred to as Primus-LM, serves as the training component within the broader Primus ecosystem.

Repository: https://github.com/AMD-AGI/Primus

What Primus provides

Area	Description
Multi-backend training	One workflow surface over Megatron-LM, TorchTitan, JAX MaxText, Megatron Bridge, and HummingbirdXT.
Unified CLI	`primus-cli` with direct (bare metal), container (Docker/Podman), and slurm (cluster) execution modes.
YAML-driven configuration	Experiment, model, module, and platform presets composed from reusable fragments under `primus/configs/`.
Benchmark suite	Built-in benchmarks (for example, GEMM) for quick hardware and stack validation.
Preflight diagnostics	Cluster-oriented checks for host, GPU, and network health before long jobs.
Performance projection	Tools to estimate memory use and throughput without occupying a full cluster.

Workflows span pretraining and post-training (including SFT and LoRA). Some Megatron configuration files expose RL-related parameters, but reinforcement-learning workflows are outside the scope of this documentation set: they are not part of the tested, supported paths described here, and this documentation does not cover how to run them.

Primus ecosystem

The training component (Primus-LM) serves as the layer between the stability and platform services above it and the low-level operator libraries below it:

                    +------------------+
                    |   Primus-SaFE    |
                    | (stability /     |
                    |  cluster mgmt)   |
                    +--------+---------+
                             |
                    +--------v---------+
                    |    Primus-LM     |
                    |  (this repo:     |
                    |   training)      |
                    +--------+---------+
                             |
                    +--------v---------+
                    |  Primus-Turbo    |
                    | (operators /     |
                    |  kernels)        |
                    +------------------+

Primus-SaFE: Stability and fault-tolerance oriented cluster management referenced by auxiliary tooling (not documented here).
Primus-LM: Training orchestration, backends, CLI, and configurations (maintained in the AMD-AGI/Primus repository).
Primus-Turbo: High-performance operators (for example, FlashAttention-style kernels, GEMM, and collectives).

Supported backends

Backend	Typical use
Megatron-LM	Broadest model coverage; default for many GPT-style and MoE recipes.
TorchTitan	PyTorch-native large-model training paths.
JAX MaxText	JAX/Flax training stacks aligned with MaxText.
Megatron Bridge	Post-training and bridge workflows on top of Megatron-related stacks.
HummingbirdXT	Additional integrated training path when enabled by your deployment.

Backend choice is specified in the configuration YAML and resolved through Primus’s adapter layer (see glossary).

Supported hardware and stack

Item	Requirement
GPUs	AMD Instinct™ MI300X, MI325X, MI355X
Platform	ROCm (version ≥ 7.0 recommended)
Container image (reference)	`docker.io/rocm/primus:v26.4`

Exact kernel and driver packages should match AMD’s documentation for your GPU SKU and ROCm release.

Key dependencies

Primus depends on the following categories of software (the list is non-exhaustive):

Category	Examples
Framework	PyTorch (Megatron-LM, TorchTitan paths); JAX/Flax (MaxText path).
AMD stack	ROCm, RCCL, HipBLASLt (GEMM), GPU drivers.
Execution	Docker or Podman for container mode; Slurm for cluster mode.
Observability & tooling	loguru (logging), Weights & Biases (`wandb`) and other optional trackers (see `requirements.txt`).

Install specifics are covered in Installation and setup.

Runtime model

At a high level, a run follows this pipeline:

YAML configuration defines work group, modules, model preset, and overrides.
primus-cli selects execution mode (direct, container, or slurm) and forwards to the runner.
Backend adapter maps the resolved configuration to the target framework (Megatron-LM, TorchTitan, and so on).
Distributed launch typically uses torchrun (or the backend’s equivalent) to start workers across GPUs and nodes.

For CLI shape and options, see Quickstart and CLI reference.

Repository layout (top level)

The following table shows the top-level layout of the Primus project repository, AMD-AGI/Primus:

Path	Role
`primus/`	Core library: configurations, runtime, trainers, backend adapters, tools (including preflight).
`runner/`	CLI implementation, helpers, hooks, and launch glue.
`examples/`	End-to-end example YAML and recipes per backend and GPU SKU.
`docs/`	Project documentation (this documentation set).
`tests/`	Automated tests.
`tools/`	Auxiliary scripts and utilities.
`benchmark/`	Benchmark drivers and related assets.
`third_party/`	Vendored or submodule dependencies.

Next steps

Installation and setup: ROCm, Docker, pip, and Slurm setup.
Quickstart: run a minimal training job in minutes.
Glossary: terms used across Primus documentation.

Licensing

Primus is distributed under the terms described in the project's LICENSE file and README. If you encounter differing license references between the README and the repository root LICENSE file, treat licensing as project-specific: confirm the intended terms with the maintainers and your own compliance process before redistributing.