A buffer-free MoE dispatch and combine method on Ascend hardware with pooled HBM cuts intermediate relay overhead via direct expert window access.
A survey on mixture of experts in large language models.IEEE Transactions on Knowledge and Data Engineering, 2025
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
MoE-Prefill achieves 1.35-1.59x higher throughput for prefill-only MoE serving by using asynchronous expert parallelism to overlap weight AllGather with computation and prefix-aware routing with true-FLOPs tracking.
DriveMoE applies scene-specialized Vision MoE and skill-specialized Action MoE to a VLA baseline to achieve SOTA closed-loop performance on Bench2Drive.
A controlled multi-model evaluation on shared data subsets shows that deployment metrics and prompting choices create important tradeoffs and alter model rankings beyond accuracy alone.
citing papers explorer
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Relay Buffer Independent Communication over Pooled HBM for Efficient MoE Inference on Ascend
A buffer-free MoE dispatch and combine method on Ascend hardware with pooled HBM cuts intermediate relay overhead via direct expert window access.
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MoE-Prefill: Zero Redundancy Overheads in MoE Prefill Serving
MoE-Prefill achieves 1.35-1.59x higher throughput for prefill-only MoE serving by using asynchronous expert parallelism to overlap weight AllGather with computation and prefix-aware routing with true-FLOPs tracking.
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DriveMoE: Mixture-of-Experts for Vision-Language-Action Model in End-to-End Autonomous Driving
DriveMoE applies scene-specialized Vision MoE and skill-specialized Action MoE to a VLA baseline to achieve SOTA closed-loop performance on Bench2Drive.
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Unified Deployment-Aware Evaluation of Open Reasoning Language Models
A controlled multi-model evaluation on shared data subsets shows that deployment metrics and prompting choices create important tradeoffs and alter model rankings beyond accuracy alone.