Google's TurboQuant AI-compression algorithm can reduce LLM memory usage by 6x

<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>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/OpenAI/comments/1s43bwo/google_unveils_turboquant_a_new_ai_memory/"> <img alt="Google unveils TurboQuant, a new AI memory

<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

<p>Google published <a href="https://arxiv.org/abs/2504.19874" rel="noopener noreferrer">TurboQuant</a> at ICLR 2026 — a technique that compresses transformer KV caches to 4 bits

<!-- 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