LLM Inference Handbook collects deployment, GPU, and optimization guidance for production teams
carrycooldude · x · 2026-07-29
LLM Inference Handbook bundles deployment guidance, GPU selection, and optimization basics
A new LLM Inference Handbook is being positioned as a single reference for engineers deploying and operating LLMs in production. It covers the full path from how inference differs from training to practical topics like:
- tokenization
- KV cache
- quantization
- speculative decoding
- disaggregated inference
- continuous batching and prefix caching
- GPU architecture and deployment patterns such as BYOC and on-prem
The handbook also emphasizes production concerns like time to first token, tokens per second, and goodput vs. raw throughput for meeting SLOs. It includes calculators, simulators, and visual tools, and is explicitly aimed at teams trying to make inference faster, cheaper, and more reliable.
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