Google DeepMind publishes a hands-on guide to scaling LLMs on TPUs and GPUs
mdancho84 · x · 2026-07-27
Google DeepMind releases a detailed guide to scaling LLMs on TPUs and GPUs
The new How to Scale Your Model guide breaks down how large language models behave on real hardware, with a focus on:
- how TPUs and GPUs work internally
- how devices communicate during training and inference
- how to choose parallelism strategies at different scales
- how to estimate training cost, serving memory, and runtime
- how to think about hardware-aware algorithm design
The authors frame scaling as something that can be reasoned about systematically rather than treated as black magic. The guide is aimed at readers with basic LLM and Transformer knowledge, plus some JAX familiarity.
More from Infra
- fmgo: call Apple's on-device Foundation Models from Go with no CGO and no Swift — Super_Run_8466 · 2026-09-23
- Huawei unveils Peerium architecture: nested BSP unifies million processors into one computer — Dr_Singularity · 2026-09-23
- Grok explains why DeepSeek picked DualPipe + ZeRO-1 over ZeRO-3 on 2048 H800s — TheZachMueller · 2026-09-23
- AI costs fall 47% per quarter, 4x faster than DNA sequencing: Epoch AI — daveholtz · 2026-09-23
- M5 Ultra LLM test: 4x faster prompt processing, but double the power draw — DigitalguyCH · 2026-09-23
- $500 of Dell OptiPlexes become a diskless netboot lab where AI agents can't brick the hardware — colinmcnamara · 2026-09-23