Show HN: Speeding up LLM inference 2x times (possibly)
Analysis
This Hacker News post presents a project aiming to speed up LLM inference by dynamically adjusting the computational load during inference. The core idea involves performing fewer weight multiplications (potentially 20-25%) while maintaining acceptable output quality. The implementation targets M1/M2/M3 GPUs and is currently faster than Llama.cpp, with potential for further optimization. The project also allows for real-time adjustment of speed/accuracy and selective loading of model weights, offering memory efficiency. It's implemented for Mistral and tested on Mixtral and Llama, with FP16 support and Q8 in development. The author acknowledges the boldness of the claims and provides a link to the algorithm description and open-source implementation.
Key Takeaways
- •Project aims to speed up LLM inference by reducing weight multiplications.
- •Offers real-time speed/accuracy adjustment.
- •Allows selective loading of model weights for memory efficiency.
- •Implemented for Mistral, tested on others, with open-source implementation.
“The project aims to speed up LLM inference by adjusting the number of calculations during inference, potentially using only 20-25% of weight multiplications. It's implemented for Mistral and tested on others, with real-time speed/accuracy adjustment and memory efficiency features.”