AI BriefingModular LLM Inference Handbook, 57 pages released for free
·2026.11.06 07:30
Key point
Modular has released a free 57-page handbook covering LLM inference metrics, optimization, and operational costs.
Details
- Modular has released the LLM Inference Handbook as a free web guide for self-hosted LLM deployment engineers
- Comprising 7 parts and 57 pages (49 pages of body text) along with 22 interactive tools, the documentation is licensed under CC BY 4.0 and the code under Apache 2.0
- It provides detailed explanations of core metrics such as TTFT, TPOT, and goodput, including their definitions, use-case priorities, and formulas for GPU memory and KV cache calculations
- It covers optimization techniques such as continuous batching, chunked prefill, PD disaggregation, and speculative decoding, as well as methods for calculating operational costs, including vendor bias analysis and Ollama error corrections
- Concepts are explained based on open-source projects and papers such as vLLM, SGLang, llm-d, and NVIDIA Dynamo, providing conditions for self-hosting advantages and an observability diagnostic table
- While it includes some content related to Modular's inference engine MAX and the Mojo language, it explicitly states that performance figures are vendor self-measured and recommends direct benchmarking
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