Democratizing MoE inference on commodity GPUs with CoMoE
Deploying Mixture-of-Experts (MoE) models relies heavily on Expert Parallelism, which generates intense inter-GPU communication. Consequently, state-of-the-art inference systems require high-bandwidth, P2P interconnects (e.g., NVLink) in datacenter GPUs to handle massive token routing, making deployment prohibitively expensive. Consumer GPUs offer comparable compute power at significantly lower cost, promising to democratize MoE inference for individuals and enable privacy-preserving local deployments. However, their bandwidth-limited (only weak PCIe bus bandwidth) and host-mediated interconnects (no P2P support) introduce severe communication bottlenecks. We present CoMoE, a communicatio...
| Authors | Ruwen Fan, Yuezhi Zu, Minhui Xie et al. |
|---|---|
| Primary category | cs.DC |
| Published | 7 October 2026 |
| Last updated at source | 9 October 2026 |
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