200lz GitHub avatar

llm-inference-optimization-lab

200lz

A two-track lab investigating LLM inference optimization through measured CPU profiling of llama.cpp and a simulated mini serving engine for scheduling, caching, and workload analysis.

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44

7-day growth

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Forks

4

Open issues

0

License

MIT

Last updated

2026-07-19

AI repository intelligence
FR-AI / ANALYSIS

Why it is worth attention

It rigorously separates measured CPU results from simulated serving outcomes, preserves negative and inconclusive findings, and provides a clean, deterministic serving simulator with detailed methodology and reproducible reports.

Who it is for

  • LLM inference optimization engineers
  • Serving system designers and researchers
  • Students learning serving architecture and performance analysis
  • Developers evaluating CPU quantization trade-offs

Use cases

  • Profiling and characterizing llama.cpp CPU inference on real hardware
  • Simulating and comparing continuous batching vs single-active FCFS policies
  • Analyzing prefix-cache and KV-cache behavior under synthetic workloads
  • Understanding the gap between microbenchmark improvements and end-to-end gains

Strengths

  • Clear evidence boundary: measured, simulated, and inconclusive results are explicitly labeled
  • Comprehensive documentation: methodology, architecture, metric definitions, and final reports for all phases
  • Deterministic serving simulator with strong invariant and lifecycle checks
  • Open-source with CI testing (GCC, Clang, Python) and reproducible build processes

Considerations

  • CPU results limited to one AMD Ryzen 7 5800H host, one small Qwen2.5 model, and one llama.cpp revision
  • Serving simulator uses a linear cost model without real tensors, tokenizer, or calibrated hardware timing
  • No GPU, accelerator, or production performance claims; inconclusive end-to-end optimization result

README quick start

Quick start

Description

Reproducible CPU LLM inference benchmarking, profiling, and hot-path optimization with llama.cpp

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