This research introduces Speculative MoE, a novel approach that significantly improves parallel inference speed for Mixture of Experts (MoE) architectures in large language models.

• Uses speculative token shuffling to optimize communication patterns
• Implements expert pre-scheduling to reduce dependency bottlenecks
• Achieves up to 1.8x speedup compared to state-of-the-art MoE inference frameworks
• Maintains high throughput while meeting latency requirements

For engineering teams, this advancement enables more efficient deployment of massive MoE models, reducing computational resource needs while maintaining performance—critical for cost-effective LLM scaling in production environments.

Speculative MoE: Communication Efficient Parallel MoE Inference with Speculative Token and Expert Pre-scheduling