| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| pretty_name: LLM Inference Engineering Notes |
| tags: |
| - text |
| - llm-inference |
| - vllm |
| - sglang |
| - kv-cache |
| - quantization |
| task_categories: |
| - text-generation |
| size_categories: |
| - n<1K |
| --- |
| |
| # LLM Inference Engineering Notes |
|
|
| A small collection of technical notes (Markdown text) on serving large language models: KV-cache math, prefill vs decode, continuous batching, quantization, speculative decoding, GPU memory sizing, latency metrics, engine comparisons and interview prep. Includes a dependency-free KV-cache / GPU-memory calculator script. This is a notes collection, not a training dataset. |
|
|
| ## Table of contents |
|
|
| - [Continuous batching explained with numbers](articles/continuous-batching-explained.md) |
| - [How much GPU memory do you need to serve a 70B model?](articles/gpu-memory-for-70b.md) |
| - [Inference engineer roadmap: skills, tools, and a 12-week plan](articles/inference-engineer-roadmap.md) |
| - [KV cache math](articles/kv-cache-math.md) |
| - [LLM inference interview questions with worked answers](articles/llm-inference-interview-questions.md) |
| - [Prefill vs decode](articles/prefill-vs-decode.md) |
| - [Quantization for serving: FP8, INT8, INT4 tradeoffs](articles/quantization-for-serving.md) |
| - [Speculative decoding: when it helps and when it doesn't](articles/speculative-decoding.md) |
| - [Time-to-first-token vs tokens-per-second: what to measure](articles/ttft-vs-tokens-per-second.md) |
| - [vLLM vs SGLang vs TensorRT-LLM: when to pick which](articles/vllm-vs-sglang-vs-tensorrt-llm.md) |
| - [KV cache calculator](calculator/kv_calc.py) ([README](calculator/README.md)) — interactive version: https://huggingface.co/spaces/inference-engineering-academy/llm-kv-cache-calculator |
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| Made by Inference Engineering Academy (https://www.inference-engineering.xyz/), which has a free Foundations course on LLM inference. |
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