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LLM Inference Handbook
A practical handbook for engineers building, optimizing, scaling and operating LLM inference systems in production.
We wrote this handbook to solve a common problem facing developers: LLM inference knowledge is often fragmented; it’s buried in academic papers, scattered across vendor blogs, hidden in GitHub issues, or tossed around in Discord threads. We’ll keep updating the handbook as the field evolves, because LLM inference is changing fast, and what works today may not be best tomorrow. If you spot an error, have suggestions for improvements, or want to add new topics, please open an issue or submit a pull request on our GitHub repository.
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