rookiemann

rookiemann / multi-turboquant

Public

Unified KV cache compression for LLM inference — TurboQuant, IsoQuant, PlanarQuant, TriAttention. 10 methods, GPU-validated, multi-GPU planner. Compress KV cache 5-80x to run bigger models, longer context, more agents on your GPU.

attention compression cuda deep-learning gpu
11
3
100% credibility
Found Apr 13, 2026 at 11 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A user-friendly toolkit that compresses the key memory cache in AI language models to enable longer conversations and more simultaneous users on limited hardware.

How It Works

1
💭 Hit a memory wall

You're chatting with a big AI model but it runs out of memory after a few long messages.

2
🔍 Find the fix

You discover a simple toolkit that squeezes AI memory so conversations can go longer without crashing.

3
📥 Get it ready

Download and set it up in moments—no complicated steps, just a few easy instructions.

4
🖥️ Open your dashboard

A friendly web page shows your computer's power and suggests perfect settings for your needs.

5
🎯 Pick your plan

Choose a ready-made option like 'balanced speed' or plan exactly how many chat buddies fit on your setup.

6
🚀 Start chatting

Copy one magic command to launch your super-efficient AI—now with way more memory room.

✨ Talk forever

Enjoy endless long conversations, multiple AIs at once, and tons of spare room—no more memory worries!

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Star Growth

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AI-Generated Review

What is multi-turboquant?

Multi-turboquant is a Python toolkit that unifies KV cache compression for LLM inference on llama.cpp and vLLM, packing 10 GPU-validated methods like TurboQuant, IsoQuant, PlanarQuant, and TriAttention into one API. It slashes KV cache memory 5-80x, letting you run bigger models, longer contexts, or more agents on a single GPU setup. Install, pick a preset like "agents_8x16k", and get exact launch commands or direct tensor compression.

Why is it gaining traction?

Unlike scattered single-method repos, this offers github unified planning for multi-GPU agents, a web dashboard for benchmarking and deployment, and presets that auto-handle calibration and compatibility across NVIDIA, AMD, and Apple Silicon. Developers love the capacity planner spitting out tensor splits and parallel slots, plus zero-calibration options like iso3 for instant wins. It's the almost unified github solution for unified cache memory bottlenecks in agent swarms.

Who should use this?

Inference engineers deploying llama.cpp servers on consumer GPUs for RAG or chat apps. Multi-agent orchestrators needing 8+ concurrent Llama 70B instances at 16k context. vLLM users experimenting with long-context workflows without OOM errors.

Verdict

Grab it for the planner and presets if you're hitting KV limits—docs are thorough, 77 tests pass, and it delivers 5-80x wins out of the box. At 11 stars and 1.0% credibility, it's alpha-stage; test thoroughly before production.

(198 words)

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