TurboQuant
New LLM compression algorithm by Google
TurboQuant launched on March 25, 2026 with 295 votes, #91 of 23,380 launches that month and more than 99% of that year's launches.
In plain words
TurboQuant is a set of quantization algorithms developed by Google designed to compress large language models and vector search engines. It offers theoretically grounded techniques that reduce model size significantly, making LLMs more practical for deployment and resource-constrained environments. The tool is intended for AI researchers, engineers, and organizations working with large language models who need to optimize memory usage and computational efficiency.
written from the facts on this page · September 2026
From the sources
A set of advanced theoretically grounded quantization algorithms that enable massive compression for large language models and vector search engines.
We introduce a set of advanced theoretically grounded quantization algorithms that enable massive compression for large language models and vector search engines. Vectors are the fundamental way AI models understand and process information. Small vectors describe simple attributes, such as a point in a graph, while “high-dimensional” vectors capture complex information such as the features of an image, the meaning of a word, or the properties of a dataset. High-dimensional vectors are incredibly powerful, but they also consume vast amounts of memory, leading to bottlenecks in the key-value cache , a high-speed "digital cheat sheet" that stores frequently used information under simple labels…from research.google
Does a similar job
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TurboQuant-WASM – Google's vector quantization in the browserApr 2026 · github.com · ▲165TurboQuant WASM SIMD vector compression — 3 bits/dim with fast dot product. Requires relaxed SIMD (Chrome 114+, Firefox 128+, Safari 18+, Node 20+) - teamchong/turboquant-wasm
TurboQuant for vector search – 2-4 bit compressionMar 2026 · github.com · ▲89A vector index built on TurboQuant, written in Rust with Python bindings - RyanCodrai/turbovec
I've built a locally running Perplexity clone2024 · github.com · ▲669The video demo runs a 7b Model on a normal gaming GPU. I think it already works quite well (accounting for the limited hardware power). :)
80% faster, 50% less memory, 0% loss of accuracy Llama finetuning2023 · github.com · ▲385Hi HN! I'm just sharing a project I've been working on during the LLM Efficiency Challenge - you can now finetune Llama with QLoRA 5x faster than Huggingface's original implementation on your own local GPU. Some highlights: 1. Manual autograd engine - hand derived backprop steps. 2. QLoRA / LoRA 80% faster, 50% less memory. 3. All kernels written in OpenAI's Triton language. 4. 0% loss in accuracy - no approximation methods - all exact. 5. No change of hardware necessary. Supports NVIDIA GPUs since 2018+. CUDA 7.5+. 6. Flash Attention support via Xformers. 7. Supports 4bit and 16bit…
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