Faster, Smaller, Smarter LLMs
Boosting LLM efficiency with self-distilled sparse drafting
SD² introduces a breakthrough approach to reduce LLM latency through optimized speculative decoding with self-distilled sparse draft models.
- Combines self-data distillation and fine-grained weight sparsity to create efficient draft models
- Significantly improves draft token acceptance rates while reducing computational costs
- Achieves remarkable efficiency gains through sophisticated engineering optimization
- Enables faster inference without compromising output quality
This research represents a critical advancement for engineering teams looking to deploy high-performance LLMs with reduced latency and resource requirements in production environments.