Gumiho introduces a novel hybrid architecture that intelligently prioritizes early tokens in speculative decoding to significantly accelerate LLM inference.

• Combines serial and parallel processing for optimal token generation
• Prioritizes early tokens that have higher likelihood of being correct
• Achieves up to 1.6x speedup over baseline speculative decoding methods
• Offers flexible deployment options with minimal overhead

This engineering innovation addresses a critical bottleneck in LLM inference, potentially enabling faster and more cost-effective AI applications in production environments.

Gumiho: A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding