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TriMoE: Augmenting GPU with AMX-Enabled CPU and DIMM-NDP for High-Throughput MoE Inference via Offloading
arXiv – CS AI|Yudong Pan, Yintao He, Tianhua Han, Lian Liu, Shixin Zhao, Zhirong Chen, Mengdi Wang, Cangyuan Li, Yinhe Han, Ying Wang||10 views
🤖AI Summary
TriMoE introduces a novel GPU-CPU-NDP architecture that optimizes large Mixture-of-Experts model inference by strategically mapping hot, warm, and cold experts to their optimal compute units. The system leverages AMX-enabled CPUs and includes bottleneck-aware scheduling, achieving up to 2.83x performance improvements over existing solutions.
Key Takeaways
- →TriMoE addresses the compute gap in MoE model inference by using a three-way GPU-CPU-NDP architecture instead of traditional two-way approaches.
- →The system categorizes experts into hot, warm, and cold groups, mapping each to optimal compute units for maximum efficiency.
- →AMX-enabled CPUs are utilized to handle warm experts that are penalized by GPU I/O latency but can saturate NDP compute throughput.
- →The architecture includes bottleneck-aware expert scheduling and prediction-driven dynamic relayout/rebalancing schemes.
- →Experimental results show up to 2.83x speedup compared to state-of-the-art MoE inference solutions.
#moe#gpu-optimization#cpu-architecture#inference-acceleration#heterogeneous-computing#amx#ndp#model-deployment
Read Original →via arXiv – CS AI
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