MoE-Hub enables seamless MoE communication overlap via hardware-accelerated destination-agnostic data transmission, delivering 1.40x-3.08x per-layer and 1.21x-1.98x end-to-end speedups over prior systems.
Gmlake: Efficient and transparent gpu memory defragmentation for large-scale dnn training with virtual memory stitching
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TokenStack's heterogeneous HBM-PIM design with base-die control and topology-aware KV placement delivers 1.62x higher geometric-mean token throughput and 1.70x SLO-compliant serving capacity than AttAcc while cutting per-token energy by 30-47%.
citing papers explorer
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MoE-Hub: Taming Software Complexity for Seamless MoE Overlap with Hardware-Accelerated Communication on Multi-GPU Systems
MoE-Hub enables seamless MoE communication overlap via hardware-accelerated destination-agnostic data transmission, delivering 1.40x-3.08x per-layer and 1.21x-1.98x end-to-end speedups over prior systems.
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TokenStack: A Heterogeneous HBM-PIM Architecture and Runtime for Efficient LLM Inference
TokenStack's heterogeneous HBM-PIM design with base-die control and topology-aware KV placement delivers 1.62x higher geometric-mean token throughput and 1.70x SLO-compliant serving capacity than AttAcc while cutting per-token energy by 30-47%.