Compression Algorithm
Methods for encoding data using fewer bits than the original representation to optimize storage, bandwidth, and computational efficiency. Critical for reducing model size, accelerating inference, and managing memory footprints in large-language-model systems.
Core Mechanisms
- Lossless Compression: Preserves exact fidelity via redundancy removal (e.g., LZ77, Huffman Coding); standard for text, code, and lossless archives.
- Lossy Compression: Sacrifices fidelity for higher ratios; prevalent in model-quantization and perceptual media.
- Entropy Encoding: Exploits statistical probabilities of data symbols.
- Transform-Based: Maps data to domains where redundancy is higher (e.g., JPEG, MP3).
AI & LLM Integration
- model-compression: Reduces weight precision (FP16 → INT8/INT4) to compress parameters and minimize VRAM usage.
- kv-cache-compression: Compresses attention keys/values to extend context windows and reduce memory bandwidth bottlenecks.
- speculative-decoding: Leverages compressed draft models to accelerate token generation; compression reduces overhead of auxiliary models.
- TurboQuant: Google-developed compression algorithm optimized for LLM inference efficiency; when coupled with Luce DFlash speculative inference engine, delivers significant acceleration and enhanced context handling for local deployments TurboQuant & DFlash: Accelerating Local LLM Inference with Enhanced Context.
Metrics
- Compression Ratio: Original size / Compressed size.
- Throughput: Processing rate post-compression.
- Fidelity Loss: Error magnitude in lossy schemes; evaluated via Bit Error Rate or downstream task degradation.