alexziskind1

alexziskind1 / draftbench

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Benchmark tool for measuring speculative decoding speedups. Sweep draft/target model combinations and generate interactive charts.

116
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100% credibility
Found Feb 18, 2026 at 67 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

draftbench is a benchmarking tool that automates testing combinations of large target AI models and smaller draft models to identify the optimal pairing for faster text generation using speculative decoding.

How It Works

1
🔍 Discover draftbench

You learn about a handy tool that tests small helper AIs with your big AI to find the fastest combo for quicker chats.

2
📥 Gather AI models

Download a few large main AIs and matching smaller helpers onto your computer.

3
📝 List combos to test

Jot down a simple plan naming your main AIs, helpers, and test settings like how much text to generate.

4
🚀 Launch the tests

Start the automatic run and watch it fire up each pair, measure speeds, and save results as it goes.

5
⏱️ Wait for results

Give it time to finish all tests, with updates showing speeds and how well each helper predicts correctly.

📈 See speedup charts

Open beautiful interactive graphs highlighting the best pairs that boost your AI speed by up to 80%.

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AI-Generated Review

What is draftbench?

draftbench is a Python benchmark tool for measuring speculative decoding speedups in LLMs using llama.cpp. It automates sweeps across target and draft model combinations on your GPU hardware, benchmarking throughput, time-to-first-token, and acceptance rates via OpenAI-compatible endpoints. You get JSON results plus interactive HTML charts—throughput bars, speedup percentages, and heatmaps—to pinpoint optimal pairings without manual testing.

Why is it gaining traction?

Unlike generic benchmark github tools, it handles full draft/target sweeps with resume support, server launching, and free GPU benchmarking tailored to llama.cpp, vLLM, or LM Studio. Developers love the JSON configs for quick hardware-specific runs (RTX or A100) and color-coded charts revealing sweet spots like 3B drafts yielding 60-80% gains on slow 72B targets. It's a no-fluff GPU benchmark tool that delivers data-driven insights fast.

Who should use this?

LLM inference engineers tuning local llama.cpp setups on Linux or Windows PCs. Hardware tinkerers testing Qwen or Llama families on consumer GPUs like RTX 4090. Researchers comparing quantizations (Q4 vs Q8) or backends for production deployment.

Verdict

Grab it if you're optimizing speculative decoding—solid docs and CLI make sweeps painless despite 49 stars and 1.0% credibility score signaling early maturity. Run a quick benchmark test on GitHub first to validate your stack; lacks tests but proves reliable for real-world GPU benchmarking.

(198 words)

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