XQuant: Unleashing LLM Inference with a Memory-Saving Breakthrough
Analysis
Key Takeaways
- •XQuant aims to reduce memory usage by recalculating KV caches instead of storing them directly.
- •This approach leverages the input activation (X) of a layer, potentially halving memory needs compared to traditional KV storage.
- •The method also facilitates low-bit quantization, further enhancing efficiency.
“XQuant's fundamental idea: Instead of directly storing KV, hold the layer's input activation X and create KV during decoding, which saves twice the memory compared to holding KV.”