Primus Projection: Estimate Memory and Performance Before You Train

2026-04-24 · Source: AMD ROCm Blogs · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Advanced, extended

Summary

AMD's Primus projection tool, released on April 24, 2026, offers analytical memory estimation and performance projection for large-scale LLM training on multi-node AMD Instinct™ GPU clusters. It addresses the high cost and complexity of distributed training by allowing users to estimate per-GPU memory usage and training throughput before committing to full-scale runs. The tool features a hierarchical memory profiler, a hybrid performance engine combining GPU benchmarks with analytical communication and pipeline schedule models, and a sub-node benchmarking methodology that downscales configurations to fit available GPUs. It supports various parallelism strategies like Tensor, Pipeline, Expert, Context, and Data Parallelism, and includes a pure analytical simulation mode for pre-silicon estimation without GPU access. Validation against Llama 3.1 and Mixtral 8x22B models on AMD Instinct™ MI325X and MI355X GPUs shows projections within 10% of measured results.

Key takeaway

For MLOps Engineers planning large-scale LLM training on AMD Instinct™ GPUs, Primus projection is essential for de-risking deployments. You should integrate its memory and performance estimation capabilities into your pre-training workflow to optimize parallelism settings and avoid costly out-of-memory errors or underutilized hardware. Start with memory projection, then use the hybrid performance projection to validate configurations, especially when scaling across multiple nodes, to ensure efficient resource allocation and predictable training times.

Key insights

Primus projection estimates LLM training memory and performance, reducing costly trial-and-error on AMD Instinct™ GPUs.

Principles

• Estimate first, then run.
• Measure what you can, simulate what you can't.
• Prioritize parallelism reduction by analytical fidelity.

Method

Primus uses a hierarchical profiler for memory and a hybrid engine for performance, combining sub-node benchmarking with analytical communication and pipeline schedule simulation, supporting CPU-only simulation for pre-silicon analysis.

In practice

• Use `projection memory` to check GPU fit.
• Benchmark on few GPUs with `--benchmark-gpus`.
• Simulate without GPUs using `--profiling-mode simulate`.

Topics

• Primus Projection
• Large Language Model Training
• Memory Estimation
• Performance Projection
• Distributed Parallelism

Code references

• ROCm/rocm-libraries
• AMD-AGI/Primus

Best for: MLOps Engineer, NLP Engineer, Machine Learning Engineer, AI Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by AMD ROCm Blogs.