[P] Implemented TurboQuant in Python

<p>Building a production-ready, fault-tolerant Retrieval-Augmented Generation system is an exercise in managing harsh tradeoffs. You want massive context, lightning-fast hybrid

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1s5cggh/google_turboquant_blew_up_for_kv_cache_heres/"> <img alt="Google TurboQuant blew up for KV cache.

<!-- SC_OFF --><div class="md"><p>Embedding layers are sensitive to quantization and Gemma 4 E2B/E4B have a ton of those which bloat the model parameter counts to 5B/10B. Makes

<!-- SC_OFF --><div class="md"><p>Just read the google recent blog post they're claiming 6x KV cache compression with zero accuracy loss and up to 8x attention speedup on H100s.

<!-- SC_OFF --><div class="md"><p>The modern ML (LLM) compiler stack is brutal. TVM is 500K+ lines of C++. PyTorch piles Dynamo, Inductor, and Triton on top of each other. I built

<!-- SC_OFF --><div class="md"><p>DeepSeek dropped the full V4 paper this week. preview from april was 58 pages, this version adds a lot of technical depth.</p> <p>What stood out

<!-- SC_OFF --><div class="md"><p>I wanted to follow up to <a

<blockquote> <p><em>Originally published at <a href="https://llmkube.com/blog/turboquant-m5-max-quality-and-asymmetric" rel="noopener