Practical Handbook for Scaling LLM Inference: A Complete Guide from Theory to Production

This is a practical handbook for LLM inference in production environments, maintained by harshuljain13 and published on GitHub (original link: https://github.com/harshuljain13/llm-inference-at-scale, updated on 2026-05-28). The handbook systematically compiles end-to-end knowledge from GPU fundamentals, attention mechanisms, quantization optimization to production deployment, filling the gap in the community's LLM inference engineering practice domain, aiming to provide a complete guide for LLM inference in production environments.

Traditional machine learning model inference is mature and stable: batch processing of requests, predictable latency, fixed memory, linear scaling. However, LLM inference breaks these assumptions:

1. Unpredictable latency (10-token response takes 100ms vs. 1000-token response takes 10s);
2. Dynamic memory demand growth (KV cache expands as tokens are generated);
3. Sublinear scaling (communication overhead dominates performance when the number of GPUs increases);
4. 100x higher cost (cost per request rises from $0.001 to $0.10). These differences gave birth to this practical handbook.

The handbook is positioned as a 'practical guide' rather than an academic compilation, integrating years of production experience and research insights. The content adopts a modular structure with a total of 8 parts:

1. Basic concepts: Analyze the four stages of tokenization/prefill/decode/detokenization, and metrics such as TTFT/ITL/throughput;
2. GPU fundamentals: HBM architecture, memory hierarchy, Roofline model, FlashAttention optimization;
3. Attention and KV cache: KV cache principles, evolution of MHA/MQA/GQA, PagedAttention and KV compression;
4. Optimization techniques: Quantization (INT8/INT4/FP8), continuous batching, speculative decoding, chunked prefill;
5. Inference engines: Comparison of architectures and tuning of vLLM/SGLang/TensorRT-LLM;
6. Large-scale deployment: Tensor parallelism, MoE inference, distillation compression, and solutions like Ray Serve/EKS+KServe/SageMaker;
7. Operation and maintenance practices: Benchmarking, structured output, edge deployment.

The handbook's key technical insights include:

1. Memory bandwidth wall: GPU computing power far exceeds memory bandwidth, so optimization should focus on reducing memory access;
2. PagedAttention: Drawing on the OS virtual memory paging mechanism, split KV cache into fixed blocks for dynamic allocation, improving GPU memory utilization;
3. Quantization trade-off: INT8 is a safe choice for performance improvement, while INT4 offers greater compression but may affect quality in sensitive tasks;
4. Speculative decoding: Use a small draft model to generate candidate tokens, then verify with the main model, which can reduce latency by 2-3 times.

The handbook provides practical production guidance:

1. Capacity planning: Calculate GPU resources based on request volume and latency requirements, balancing cost and performance;
2. SLO management: Set target metrics like TTFT/ITL, monitor and diagnose deviation issues;
3. Engine selection: vLLM is suitable for high-throughput scenarios, SGLang excels at structured output, and TensorRT-LLM optimizes performance on NVIDIA hardware.

The project adopts an open contribution model, and the community is welcome to submit PRs. The author continuously updates content through the Substack column 'The Engineer's Digest' to ensure the handbook keeps up with new technologies in the LLM inference field (such as FP4 quantization, new attention mechanisms).

This handbook bridges the gap between academic papers and production practice, integrating scattered knowledge into a systematic resource. Whether you are a novice just getting started with LLM inference or an experienced engineer optimizing existing systems, you can gain valuable insights from it. It is a precious resource for engineers deploying LLMs to production environments.