TurboQuant-H: A Technique For Quantizing Models Like Gemma 4 E2B/E4B to 2-bit

<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>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>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>&#x200b;</p> <p>## Abstract</p> <p>Post-training makes language models more decisive without necessarily making them more accurate — and we find

<!-- SC_OFF --><div class="md"><p>Spent ~2 days implementing this paper: <em>TurboQuant: Online Vector Quantization with Near-optimal Distortion Rate</em></p> <p>Repo: <a

<table> <tr><td> <a href="https://www.reddit.com/r/singularity/comments/1s4gwnq/turboquant_redefining_ai_efficiency_with_extreme/"> <img alt="TurboQuant: Redefining AI efficiency

<!-- SC_OFF --><div class="md"><p>Got inspired to try and crack this egg without using heretic.</p> <p>FP16, Q8_0 and Q4_K_M quants, plus the abliteration script for