jranaraki / vllm-tuner

What is vllm-tuner?

vllm-tuner is a Python tool that automatically tunes vLLM serving parameters like batch size, max batched tokens, and GPU memory utilization to boost throughput while cutting latency and memory use. You feed it a YAML config with your model, workload (like Alpaca prompts), and objectives, then run `vllm-tuner tune` for Bayesian optimization across trials, complete with GPU metrics monitoring and interactive Plotly HTML reports. It solves the pain of manually tweaking vLLM configs through endless trial-and-error on your inference servers.

Why is it gaining traction?

Unlike generic hyperparameter tools, it integrates deeply with vLLM logs for KV cache stats and preemptions, supports multi-GPU setups, and generates baselines plus Pareto fronts out of the box. Developers dig the CLI simplicity, constraint handling (e.g., max latency caps), and rich outputs like trial summaries in JSON/YAML/HTML—no more eyeballing server metrics manually. At 44 stars, it's niche but hooks vLLM users tired of suboptimal defaults.

Who should use this?

ML engineers deploying production vLLM servers for chatbots or intelligent trading bots, ops teams on GPU clusters optimizing inference for low-latency apps, or researchers benchmarking models like Qwen or Llama. Ideal if you're scaling intelligent systems on GitHub projects needing automatic parameter optimization without deep vLLM expertise.

Verdict

Grab it if you're running vLLM today—solid alpha with excellent README, CLI, and reports, despite 1.0% credibility and low stars signaling early maturity. Test on a single GPU first; it'll pay off quickly for perf gains.

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