2.4x Faster Token Generation on CPU – Without Sacrificing Persona (github.com)

A new fork of llama.cpp has been announced, achieving a significant 2.4x increase in token generation speed on CPU without sacrificing model personality. This enhancement utilizes Early-Exit Patches and path-trained LoRAs, allowing it to replace heavyweight models like Q8_0 with a lightweight Q2_K base model. By leveraging the Early-Exit Patch, the system can skip repeated computations once confidence thresholds are achieved, effectively doubling the speed of token generation while preserving the nuanced characteristics of different personas. In performance tests conducted on an 8-thread CPU environment, the optimized setup demonstrated remarkable results, with token generation rates increasing from 4.94 to 11.98 t/s, while maintaining over 95% of the richness traditionally associated with Q8 models. The integration of LoRAs enhances roleplay consistency, acting as a guide for maintaining character depth without the typical disclaimers associated with standard AI responses. This development is notably significant for the AI/ML community, as it highlights a viable pathway to enhance real-time conversational capabilities in language models, paving the way for more engaging and responsive AI applications.