Author(s): Algomaster

Originally published on Towards AI.

Implementing Google Research’s TurboQuant algorithm on MLX- for 5× KV cache compression confirmed, quality benchmarks coming in Part 2

Local LLMs on Apple Silicon face one hard constraint: unified memory is finite. A 26B parameter model consumes ~27 GB for weights alone. Every token generated appends to the KV cache — and at context lengths above 16K, that cache can rival the model size itself. TurboQuant — a quantization algorithm published by Google Research — compresses KV cache entries from 16-bit floats down to 3-bit indices with near-zero quality loss. I have implemented it natively for Apple’s MLX framework and benchmarked it on two hybrid-architecture models: Gemma 4 26B and Qwen3 9B. Results: 5.22× compression on Gemma 4 and 5.12× on Qwen3, stable across all tested prompt lengths.

The article discusses the implementation of Google Research’s TurboQuant algorithm aimed at improving the efficiency of local large language models on Apple Silicon by significantly reducing memory overhead linked to key-value (KV) caches. It highlights the effectiveness of this quantization method in achieving impressive compression ratios across different architectures, specifically offering insights into the performance of models such as Gemma 4 and Qwen 3.5, while also hinting at future explorations into the implications of these compressions on model output quality.

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Published via Towards AI