How To Scale Your Model: The Math of Memory, Batching and Multi-Accelerator Inference
techNmak · x · 2026-10-17
Entry 4 in an LLM inference learning list: All About Transformer Inference from the Google DeepMind JAX team. It covers the math behind memory, batching, and multi-accelerator inference with worked problems, building from sampling a single token all the way to a scaled inference engine — and notes that research codebases often leave big wins on the table in the inference path. Demanding but worth it.
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