tonbistudio

tonbistudio / turboquant-pytorch

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From-scratch PyTorch implementation of Google's TurboQuant (ICLR 2026) for LLM KV cache compression. 5x compression at 3-bit with 99.5% attention fidelity.

47
6
100% credibility
Found Mar 25, 2026 at 47 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A ready-to-test toolkit that compresses AI language models' temporary memory to support much longer inputs with little quality loss.

How It Works

1
🔍 Discover TurboQuant

You come across this clever trick that squeezes an AI's working memory to handle much longer stories without needing more power.

2
🛠️ Get your setup ready

You gather the simple everyday tools on your computer to start experimenting, like having Python and a few helpers.

3
🧪 Run quick practice checks

You launch the easy tests with pretend data to see how well the memory squeeze works right away.

4
📉 See the memory magic

Watch in amazement as the tests show 3 to 7 times smaller memory use while keeping matches super accurate.

5
🤖 Try it on a real AI

You load a small language model, feed it a long hidden-fact story, and apply the squeeze to its memory.

6
✅ Check the results glow

The AI still focuses on the right spots almost perfectly, proving longer chats are now possible.

🎉 Unlock longer AI adventures

Your AI now remembers epic lengths of text, opening doors to smarter, bigger conversations.

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Star Growth

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AI-Generated Review

What is turboquant-pytorch?

Turboquant-pytorch is a from-scratch PyTorch implementation of Google's TurboQuant algorithm for compressing LLM key-value caches, slashing memory use by up to 7x while keeping attention scores 99.5% faithful at 3-bit quantization. It solves the KV cache bottleneck on GPUs like RTX 3060, where long contexts (8K+ tokens) eat VRAM faster than model weights. Developers get drop-in cache wrappers, synthetic test scripts, and real-model validation on Qwen2.5-3B—run `python -m turboquant.test_turboquant` or `validate` to check claims yourself, much like llama from scratch pytorch or mamba from scratch pytorch repos.

Why is it gaining traction?

It beats alternatives by hitting paper bounds on synthetic vectors and real attention fidelity (cosine sim 0.995 at 5x compression), with asymmetric estimators computing scores directly from compressed data—no decompression overhead. The hook: precomputed codebooks, GPU benchmarks, and needle-in-haystack tests prove it preserves retrieval in long sequences, standing out in the from scratch github crowd alongside nerf from scratch pytorch or yolo from scratch pytorch.

Who should use this?

LLM inference engineers tuning long-context generation on 12GB GPUs, like deploying Qwen or Llama models beyond 8K tokens. Researchers prototyping KV compression for custom heads (64-256 dim), or teams evaluating ml from scratch github tools before production.

Verdict

Grab it for proofs-of-concept—solid docs, tests, and validation scripts make evaluation dead simple despite 47 stars and 1.0% credibility score signaling early maturity. Production? Wait for more integrations and edge-case coverage.

(198 words)

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