OmarHory

OmarHory / turboquant

Public

Open-source implementation of Google's TurboQuant (ICLR 2026) — KV cache compression to 2.5–4 bits with near-zero quality loss. 3.8–5.7x memory reduction on Mistral-7B, no training required.

46
8
100% credibility
Found Apr 08, 2026 at 47 stars -- GitGems finds repos before they trend. Get early access to the next one.
Sign Up Free
AI Analysis
Python
AI Summary

TurboQuant is an open-source implementation of a research technique that compresses the memory caches used by large language models, achieving 3.8–5.7x reductions with near-identical output quality.

How It Works

1
🔍 Discover TurboQuant

You hear about TurboQuant from a research blog or paper, a clever way to make AI chatbots use way less memory without messing up their smarts.

2
💻 Get it on your computer

Download the files to your laptop and prepare the simple tools it needs, like setting up a quiet workspace for testing.

3
🧪 Run your first memory test

Start a quick check on your own computer to see how much space AI helpers save when chatting.

4
⚡ Watch the magic happen

Your screen shows huge memory savings—like shrinking a balloon—while the AI still gives smart, correct answers.

5
Pick your power level
🏠
Stay local

Use your laptop for small tests—easy and free.

🚀
Go cloud

Borrow powerful gear online to handle giant AI brains.

6
📊 Check the answers

Run fun tests like hiding a secret fact in a long story and see if the AI finds it perfectly.

🎉 AI runs faster and leaner

Celebrate as your AI now fits in tiny spaces, thinks quicker, and handles long talks without forgetting—ready for real use!

Sign up to see the full architecture

5 more

Sign Up Free

Star Growth

See how this repo grew from 47 to 46 stars Sign Up Free
Repurpose This Repo

Repurpose is a Pro feature

Generate ready-to-use prompts for X threads, LinkedIn posts, blog posts, YouTube scripts, and more -- with full repo context baked in.

Unlock Repurpose
AI-Generated Review

What is turboquant?

TurboQuant is a Python library that compresses KV caches in large language models to 2.5-4 bits per value, slashing memory use by 3.8-5.7x on models like Mistral-7B while keeping generation quality intact—no training or calibration needed. It plugs directly into Hugging Face Transformers as a drop-in cache replacement for inference. Developers get scripts for local CPU benchmarks, Needle-in-a-Haystack tests, LongBench evals, and even one-click GPU runs on RunPod.

Why is it gaining traction?

Unlike typical quantization tools requiring finetuning or calibration data, TurboQuant delivers near-optimal compression out-of-the-box via provable math from the Google Research paper, validated against theoretical bounds. It includes GPU attention speedups up to 1.85x and outlier-aware modes for aggressive 2.5-bit setups without coherence loss. As an open-source implementation of TurboQuant, it stands out among github open source tools for LLM serving, with easy repro of paper results.

Who should use this?

Inference engineers deploying long-context LLMs on A40/A100 GPUs or edge hardware, especially for Mistral-7B or Llama-3.1-8B where KV cache balloons at 16K+ tokens. Teams benchmarking memory-speed tradeoffs before production, or researchers validating compression on evals like LongBench-E and needle retrieval.

Verdict

Grab it for experiments if you're optimizing LLM serving—benchmarks and evals make it dead simple to test, and it passes 30/30 paper checks. At 46 stars and 1.0% credibility, it's early and lacks broad testing, so pair with your own QA before prime time.

(198 words)

Sign up to read the full AI review Sign Up Free

Similar repos coming soon.