Feeding Rubin's tensor cores took more than wider MMAs: reuse and deeper pipelines
togethercompute · x · 2026-09-11
Follow-up in the same thread: Rubin's tensor cores can consume operands twice as fast, but simply widening the MMA wasn't enough — the team had to increase data reuse, deepen the pipeline, and more to keep the cores fed, ultimately pushing their 16k NVFP4 GEMM to 22.4 PFLOPS.
Related event: ThunderKittens Lands on Vera Rubin: NVFP4 GEMM Tops 22 PFLOPS(7 posts)→
More from Infra
- Anthropic's $430B compute contracts are noncancelable, topping China's military budget — xiaosun86 · 2026-10-27
- AI Infra Meme: While Countries Need TW-Scale Power for Datacenters, People Argue About Lightbulbs — mgostIH · 2026-10-27
- Legora runs 2,000 parallel agent evals on Modal Sandboxes for legal AI — blelbach · 2026-10-27
- Cloud release velocity scores: AWS 95, Azure 90, Google Cloud 82 — DavidLinthicum · 2026-10-27
- Selling a 5090 to scale local inference: one business weighs 5 hardware paths — Anarchaotic · 2026-10-27
- AWS tutorial: fleet-wide edge CV model lifecycle with SageMaker SDK v3, ~$0.07 per run — AWS ML Blog · 2026-10-27