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Ryuketsukami / turboquant-compression

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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.

python deep-learning numpy transformer attention quantization vector-quantization model-compression inference-optimization memory-optimization kv-cache post-training-quantization llm llm-inference llm-compression kv-cache-compression iclr-2026 turboquant polarquant qjl
  • Updated Mar 28, 2026
  • Python

Ryuketsukami / turboquant-skill

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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.

python deep-learning transformer quantization codex vector-quantization model-compression inference-optimization kv-cache ai-agent llm llm-compression kv-cache-compression claude-code coding-agent iclr-2026 agent-skill turboquant polarquant qjl
  • Updated Mar 28, 2026
  • Python

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