Samoyeds introduces a novel approach that accelerates Mixture-of-Experts (MoE) models by exploiting structured sparsity patterns in both model parameters and activations.

• Achieves up to 9.2x speedup in end-to-end inference using specialized sparse computing hardware
• Develops custom kernels and data formats optimized for Sparse Tensor Cores
• Maintains model accuracy while significantly reducing computational demands
• Demonstrates practical implementation on NVIDIA Ampere and Hopper GPUs

Why it matters: As MoE-based LLMs continue to grow in size and complexity, these engineering innovations enable more efficient deployment without sacrificing performance, potentially reducing infrastructure costs and energy consumption for AI systems.

Samoyeds: Accelerating MoE Models with Structured Sparsity Leveraging Sparse Tensor Cores