TurboQuant, KV cache x6 less memory and X8 faster with zero accuracy loss

<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

<!-- 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.

<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>TurboQuant is amazing, memory isn't needed anymore, everyone needs to sell sell sell before it's worth nothing.</p> <p>Just keep that narrative

<!-- SC_OFF --><div class="md"><p>Hello,</p> <p>Anyone knows what are the differences and if they are really doing the job they say? Because i was watching something about

<!-- 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

&#32; submitted by &#32; <a href="https://www.reddit.com/user/DeltaSqueezer"> /u/DeltaSqueezer </a> <br /> <span><a

<!-- SC_OFF --><div class="md"><p>I applied video compression to LLM inference and got <strong>10,000x less quantization error at the same storage cost</strong></p> <p><a