An open handbook on LLM inference at scale (GPU internals, KV cache, batching, vLLM/SGLang/TensorRT-LLM) [P]

This handbook covers the technical aspects of running large language models efficiently at scale, focusing on GPU optimization techniques including GPU internals, key-value (KV) cache management, batch processing strategies, and popular inference frameworks like vLLM, SGLang, and TensorRT-LLM. It serves as an open educational resource for understanding the systems-level considerations required for deploying LLMs in production environments. The guide likely bridges the gap between theoretical LLM knowledge and practical implementation details needed for optimized inference performance.

Source: r/MachineLearning | 2026-06-20