Google DeepMind team publishes deep dive on scaling Transformer inference
zainhas · x · 2026-09-13
Google DeepMind authors (Jacob Austin, Sholto Douglas, Roy Frostig, et al.) released Part 7 of "How To Scale Your Model": a systematic guide to Transformer inference.
- Key difference from training: latency becomes a first-class concern; research codebases often leave lots of low-hanging fruit in the inference path
- Starts from naive next-token sampling (re-processing the whole prefix, Θ(n²) runtime)
- Builds up to KV cache, generation loops, and efficiently scaling a large Transformer across many accelerator slices in an inference engine
A rare, complete systems treatment for anyone working on inference optimization and serving stacks.
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