Building TurboQuant Vector Search on Apple Silicon: What I Learned

<p>Your RAG application works perfectly with 1,000 documents. You push it to production, upload 10 million vectors, and suddenly your query latency jumps from 50ms to 5 seconds.

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

<p><em>A deep-dive into the latest FerresDB updates — from HNSW auto-tuning and PolarQuant compression to Point-in-Time Recovery, cross-encoder reranking, and a distributed Raft

<!-- 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

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<!-- SC_OFF --><div class="md"><p>We spent the last year building a code indexing system and tried pretty much every retrieval approach along the way. Sharing what actually worked

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1s92x7z/attnrot_ggerganovs_turboquant_lite_is_on_the_cusp/"> <img alt="attn-rot (ggerganov's

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1t5g7cf/followup_trying_to_make_nvidia_gpus_plugandplay/"> <img alt="Follow-up: Trying to make NVIDIA GPUs