Dynamis-Labs / spectralquant

What is spectralquant?

Spectralquant compresses KV caches for large language model inference, slashing memory use while preserving attention quality. It runs a one-time 15-second calibration on your data to spot the 3-4% of key dimensions carrying signal across models like Qwen, Llama, Mistral, and Gemma, then skips costly error correction on the noisy rest. Built in Python with PyTorch, it delivers 5.95x compression and 2.2x faster latency than TurboQuant baselines on Qwen 2.5-14B.

Why is it gaining traction?

It breaks TurboQuant's limits by exploiting a universal spectral low-rank property in keys—d_eff stays ~3% regardless of model size or architecture. Developers get higher cosine similarity (0.9485 vs 0.9226), perplexity-neutral generation, and speedups at all sequence lengths without retraining. Even if github you do not have permission to push to the head branch or you have divergent branches, the reproducible experiments and paper PDF make it easy to verify claims yourself.

Who should use this?

Inference engineers deploying LLMs on GPUs like B200, optimizing long-context serving for chatbots or agents. Quantization researchers benchmarking KV compression, or teams hitting memory walls in production—especially if you're evaluating Qwen/Llama and need github you have codespaces that will soon be deleted warnings out of the way for quick prototypes.

Verdict

Intriguing research prototype with strong empirical wins, but 21 stars and 1.0% credibility score signal early-stage code—solid docs, tests, Makefile, but no prod polish. Fork it if you need the drugs lyrics-level hype for spectral tricks; otherwise, monitor for maturity. Worth a spin for inference tinkerers. (198 words)