KV Cache with PagedAttention vs PagedAttention + TurboQuant - experiments across token sizes comparing memory, latency, and accuracy.
Compress embeddings 6x instantly with TurboQuant. First pip package using Google's TurboQuant (ICLR 2026) for vector search. 71.9% recall vs FAISS PQ 13.3%.
TurboQuant (ICLR 2026) vector quantization for memory/RAG embedding compression | 5-8x压缩 98%+召回率 | numpy only, no GPU
LangChain VectorStore with TurboQuant compression (ICLR 2026) - 6x memory reduction, training-free, no GPU required. The first LangChain integration for Google Research's TurboQuant algorithm.
CommitMind: Semantic search for Git commit history powered by TurboQuant vector compression (ICLR 2026). Search commits by meaning, not just keywords.
ChatMind: Semantic search for Discord & KakaoTalk chat messages. Search by meaning, not keywords. Powered by TurboQuant compression (ICLR 2026).
AI Code Review Memory - learns from your team's bug history and warns when similar patterns appear
AI-powered log anomaly detection CLI — learns normal patterns, detects anomalies with semantic embeddings, matches past incidents. Powered by TurboQuant 3-bit compression (ICLR 2026).
Interactive Benchmarking Tool for TurboQuant KV Cache Compression. Supports 2-4 bit quantization with Real-time Metrics
AI-powered semantic search for Obsidian notes - Find notes by meaning, not keywords. Uses sentence-transformer embeddings + TurboQuant 3-bit compression.
Near-optimal vector quantization for LLM KV cache compression. Python implementation of TurboQuant (ICLR 2026) — PolarQuant + QJL for 3-bit quantization with minimal accuracy loss and up to 8x memory reduction.
AI agent skill implementing Google's TurboQuant compression algorithm (ICLR 2026) — 6x KV cache memory reduction, 8x speedup, zero accuracy loss. Compatible with Claude Code, Codex CLI, and all Agent Skills-compatible tools.
TurboQuant‑style embedding compression for RAG: an SDK using fixed rotations, PolarQuant, and QJL residual sketches for compact storage and fast similarity search
Near-optimal vector quantization from Google's ICLR 2026 paper — 95% recall, 5x compression, zero preprocessing, pure Python FAISS replacement
First open-source implementation of TurboQuant (arXiv 2504.19874) — 4-7x LLM KV cache compression. pip install turbokv
Experimental TurboQuant implementation and llama.cpp-style integration path for long-context inference
Near-optimal vector quantization for OpenClaw context compression, based on the TurboQuant algorithm from Google Research.
Implementation of https://research.google/blog/turboquant-redefining-ai-efficiency-with-extreme-compression/
your ai, your rules. — local AI desktop app with hardware-aware model matching, threaded conversations, and TurboQuant integration. no cloud, no subscription, no data leaving your device.
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