Primus Projection Cuts Distributed LLM Training Tuning to Single-Node Simulation
Teams burning GPU-hours on OOM crashes and suboptimal parallelism recipes can instead score candidate configurations in seconds on one machine, then commit only to the promising ones. The paper's validated error rates at production scale — including on DeepSeek V3 and Qwen3 — make the projections credible for real deployments. Engineers planning training runs on 256 to 1,024 GPU clusters, or on hardware not yet available, get a practical tuning workflow they can adopt directly.