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Huawei CloudMatrix384/Infer: pooled supernode resources and PDC serving

Contribution grade

Disaggregated LLM serving systems · Human-designed research method or resource

Reference score75,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Co-designed a Unified-Bus supernode with independently scalable prefill, decode and caching pools, exploiting direct remote-memory access and expert-parallel serving for large MoE models.

Scientific scope

Co-designed a Unified-Bus supernode with independently scalable prefill, decode and caching pools, exploiting direct remote-memory access and expert-parallel serving for large MoE models. Prefill/decode separation, MoE and cache pooling have predecessors; contribution is the specified hardware/software integration. Network numbers can be per die rather than per dual-die NPU and unidirectional rather than bidirectional. Provider comparisons vary latency SLO, concurrency, precision, parallelism and hardware; no universal accuracy/throughput claim or independent replay. Original46-person Huawei/SiliconFlow affiliations are preserved.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Prefill/decode separation, MoE and cache pooling have predecessors; contribution is the specified hardware/software integration. Network numbers can be per die rather than per dual-die NPU and unidirectional rather than bidirectional. Provider comparisons vary latency SLO, concurrency, precision, parallelism and hardware; no universal accuracy/throughput claim or independent replay. Original46-person Huawei/SiliconFlow affiliations are preserved.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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