Why exactly can't we use the techniques in TurboQuant on the model's quantizatio

<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>Does turboquant need any actual arch changes to a model or is it just a different method of representing kv cache and can all be done in

<!-- 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>which model quantization technique is best suitable for smartphones at this point...specially if the model is finetuned as that tends to amplify