EasySpec introduces a novel layer-parallel approach to speculative decoding that eliminates GPU idle time and optimizes multi-GPU utilization for faster LLM inference.

• Addresses the GPU idling problem during traditional speculative decoding
• Implements a layer-parallel strategy where draft and base models operate simultaneously
• Achieves up to 2.2x throughput improvement over standard tensor parallelism
• Requires minimal code changes to existing systems

This engineering advancement matters because it makes LLM inference more cost-effective and responsive at scale, enabling faster deployment of large language models in production environments without sacrificing accuracy.

EasySpec: Layer-Parallel Speculative Decoding for Efficient Multi-GPU Utilization