ArmanJR

ArmanJR / PrismML-Bonsai-vs-Qwen3.5-Benchmark

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PrismMl Bonsai vs Qwen3.5 Benchmark

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100% credibility
Found Apr 03, 2026 at 44 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

This project benchmarks Bonsai-8B against Qwen3.5 model variants on a Jetson Orin, measuring accuracy across categories and generation speed, with scripts to run tests and generate analysis plots.

How It Works

1
🔍 Discover the Comparison

You find a cool test pitting a super-tiny AI brain against popular smart helpers on a small powerful gadget.

2
📖 Explore the Setup

Read how it measures smarts on facts, math, coding, history, logic, language, and even another tongue across easy to hard questions.

3
⚙️ Prepare Your Device

Get your mini computer ready by adding the different AI helpers so they can run side by side.

4
▶️ Launch the Challenge

Start the race where each AI tackles 98 questions three times to show their true speeds and accuracies.

5
📊 Make Picture Results

Create easy-to-read charts and graphs breaking down winners by speed, smarts, categories, and more.

🎉 Choose Your Favorite

Celebrate understanding which AI fits your needs best—quick and small or super smart and steady.

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Star Growth

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AI-Generated Review

What is PrismML-Bonsai-vs-Qwen3.5-Benchmark?

This Python benchmark pits PrismML's Bonsai-8B 1-bit LLM against Qwen3.5 models from 0.8B to 35B on a Jetson Orin edge device, running 98 questions across math, coding, reasoning, and more. It measures accuracy, speed (tok/s), and efficiency (accuracy per GiB), spitting out CSV results and detailed plots for easy analysis. Developers get a CLI to fire up the full suite or cherry-pick models, solving the pain of guessing quantization tradeoffs on memory-limited hardware.

Why is it gaining traction?

It delivers real-world Jetson Orin numbers—no cloud fluff—with breakdowns by category, difficulty, and speed-vs-accuracy scatter plots that reveal Bonsai's coding prowess (100% score) despite 1-bit compression. Unlike generic leaderboards, this focuses on edge inference viability, highlighting how Bonsai beats Qwen3.5-2B in accuracy at similar size while tripling speed over larger siblings. The generated visuals and key takeaways make it dead simple to spot sweet spots like Qwen3.5-9B for balanced dense performance.

Who should use this?

Edge ML engineers tuning LLMs for Jetson or similar ARM devices with tight RAM budgets. Quantization researchers comparing 1-bit extremes like Bonsai to Q4_K_M baselines on reasoning-heavy tasks. Production devs prototyping fast inference pipelines where coding or factual Q&A trumps multilingual depth.

Verdict

Grab it for quick directional insights on Bonsai vs Qwen3.5—solid docs and plots punch above its 44 stars and 1.0% credibility score—but treat results as experimental signals, not gospel; it's a rough benchmark needing broader hardware validation before guiding deployments.

(198 words)

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