WeianMao

WeianMao / triattention

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TriAttention — Efficient long reasoning with trigonometric KV cache compression. Enables OpenClaw local deployment on memory-constrained GPUs.

455
34
100% credibility
Found Apr 12, 2026 at 455 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

TriAttention compresses memory in AI models for long reasoning tasks like math, delivering up to 2.5x faster performance with matching accuracy via vLLM integration.

How It Works

1
📖 Discover TriAttention

Stumble upon this clever tool while searching for ways to make AI math solvers handle super long problems without slowing down.

2
🛠️ Set it up easily

Follow a few simple steps to install it on your computer, no complicated setup needed.

3
⬇️ Grab test models

Pick a ready-to-use AI brain and math puzzles that download with one command.

4
🔧 Tune for speed

Create a quick profile that squeezes memory use while keeping answers spot-on accurate.

5
📈 Test the magic

Run challenges and watch it solve tough math 2.5 times faster with no mistakes.

6
🌐 Go live online

Turn it into a web service that apps can chat with instantly.

🚀 Supercharge your AI

Now your math-solving assistant tackles marathon problems blazing fast, saving time and memory.

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Star Growth

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AI-Generated Review

What is triattention?

TriAttention is a Python library for KV cache compression that slashes memory use by up to 10.7x during long reasoning tasks, using trigonometric techniques to keep full accuracy. It plugs into vLLM for seamless deployment, enabling local runs of models like Qwen3-8B on memory-constrained GPUs via an OpenAI-compatible API. Set a few env vars like TRIATTN_RUNTIME_KV_BUDGET, and it handles compression transparently.

Why is it gaining traction?

It delivers 2.5x throughput boosts on benchmarks like AIME25 without accuracy loss, outpacing SnapKV and R-KV on long-context math problems. The vLLM plugin requires zero code changes, and OpenClaw support makes efficient local GPU inference dead simple. Developers dig the precomputed stats for popular models and quick CLI benchmarks.

Who should use this?

AI engineers deploying reasoning models locally on RTX 4090s or similar, especially for math, coding, or long-context chains. Ideal for indie devs or teams optimizing vLLM servers on edge hardware where full KV cache blows memory limits.

Verdict

Worth trying for memory-pinched long reasoning setups—strong docs, vLLM integration, and 455 stars show promise despite the 1.0% credibility score signaling early maturity. Test on your workloads before production.

(198 words)

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