VQKV: High-Fidelity and High-Ratio Cache Compression via Vector-Quantization
VQKV: High-Fidelity and High-Ratio Cache Compression via Vector-Quantization explores VQKV is a training-free method for high-fidelity KV cache compression using vector quantization.. Commercial viability score: 3/10 in Cache Compression.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Prior WorkLeech Lattice Vector Quantization for Efficient LLM CompressionViability: 3.0
- Prior WorkSideQuest: Model-Driven KV Cache Management for Long-Horizon Agentic ReasoningViability: 3.0
- Prior WorkLongFlow: Efficient KV Cache Compression for Reasoning MViability: 3.0
- Higher ViabilityKVSculpt: KV Cache Compression as DistillationViability: 7.0
- Higher ViabilityMore Than a Quick Glance: Overcoming the Greedy Bias in KV-Cache CompressionViability: 5.0