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3 curated events
papersTODAY 04:00 UTC

arXiv Paper Proposes Partitioned KVCache Format to Balance CPU-GPU Load in MoE Inference

A new arXiv preprint describes a key-value cache layout that is physically split so work can be divided between CPU and GPU during long-context inference with Mixture-of-Experts models. The approach targets the case where single-GPU memory is insufficient and the KV cache must spill into host memory, where the stored data either feeds attention on the GPU or supports computation on the CPU. The proposed format is designed to keep those two uses balanced rather than treating the spilled cache as a single monolithic block.

inference/kv-cache/moe/arxiv
papersSEP 11 04:00 UTC

FluxMoE: Decoupling Expert Residency for High-Performance MoE Serving

A new arXiv paper proposes FluxMoE, a serving system that separates where mixture-of-experts weights live from GPU memory constraints. Existing inference engines keep every expert resident on GPUs, which competes for space with the key-value cache and limits throughput. The work targets higher-performance MoE inference by changing how expert residency is managed.

moe/inference/efficiency/arxiv
papersSEP 10 04:00 UTC

Researchers propose a method to preserve long-tailed expert knowledge in MoE fine-tuning

A new arXiv paper tackles a weakness in adapting Mixture-of-Experts models: routing layers can destabilise during supervised fine-tuning, causing rarely used experts to lose their specialised knowledge. The authors introduce a tuning approach designed to retain this long-tailed expert information and compare it with earlier anti-collapse techniques such as DenseMixer and ESFT. The work addresses a practical bottleneck for teams adapting large MoE models to downstream tasks.

moe/fine-tuning/llms/routing