Huawei CloudMatrix: A Peer-to-Peer AI Datacenter Architecture for Scalable and Efficient LLM Serving

LLMs have quickly superior with hovering parameter counts, widespread use of mixture-of-experts (MoE) designs, and large context lengths. Fashions like DeepSeek-R1, LLaMA-4, and Qwen-3 now attain trillions of parameters, demanding monumental compute, reminiscence bandwidth, and quick inter-chip communication. MoE improves effectivity however creates challenges in professional routing, whereas context home windows exceeding 1,000,000 tokens pressure consideration and KV cache storage, which scales with concurrent customers. In real-world deployments, unpredictable inputs, uneven professional activations, and bursty queries additional complicate serving. Addressing these pressures requires a ground-up rethinking of AI infrastructure by {hardware}–software program co-design, adaptive orchestration, and elastic useful resource administration. 

Current progress in LLMs is formed by three most important traits: ever-growing parameter counts, sparse MoE architectures, and prolonged context home windows. Fashions like Llama 4, DeepSeek-V3, and Google’s PaLM push scale into the trillions of parameters, whereas MoE designs activate solely subsets of specialists per token, balancing effectivity with capability. In the meantime, context home windows now span tons of of 1000’s to thousands and thousands of tokens, enabling long-form reasoning however straining compute and reminiscence by giant key-value caches. These advances place immense strain on datacenters, demanding greater compute, reminiscence, and bandwidth whereas introducing challenges in parallelism, workload heterogeneity, knowledge convergence, and storage efficiency. 

Huawei researchers launched CloudMatrix, a brand new AI datacenter structure designed to deal with the rising calls for of large-scale LLMs. Its first implementation, CloudMatrix384, combines 384 Ascend 910C NPUs and 192 Kunpeng CPUs, all linked by a high-bandwidth, low-latency Unified Bus that permits absolutely peer-to-peer communication. This design permits versatile pooling of compute, reminiscence, and community sources, making it best for MoE parallelism and distributed KV cache entry. On prime of this, CloudMatrix-Infer gives an optimized serving framework with peer-to-peer useful resource swimming pools, large-scale professional parallelism, and hardware-aware optimizations like pipelining and INT8 quantization. Evaluations with DeepSeek-R1 present state-of-the-art throughput, effectivity, and scalability. 

Huawei CloudMatrix is a brand new AI datacenter structure constructed on peer-to-peer high-bandwidth interconnects and fine-grained useful resource disaggregation. Its first large-scale implementation, CloudMatrix384, integrates 384 Ascend 910C NPUs and 192 Kunpeng CPUs right into a single supernode, all linked by a unified bus community that permits direct all-to-all communication. This design permits compute, reminiscence, and community sources to be shared seamlessly and scaled independently, working as one cohesive system. By avoiding the bottlenecks of conventional hierarchical setups, CloudMatrix384 is especially efficient for communication-heavy duties equivalent to large-scale MoE parallelism and distributed KV cache administration, making it best for scalable LLM serving. 

The researchers consider CloudMatrix-Infer on the DeepSeek-R1 mannequin utilizing the CloudMatrix384 supernode. The system achieves a prefill throughput of 6,688 tokens per second per NPU and a decode throughput of 1,943 tokens per second with latency saved beneath 50 ms, outperforming comparable programs equivalent to SGLang on NVIDIA H100 and DeepSeek on H800. Even when constrained to stricter latency necessities of beneath 15 ms, it sustains 538 tokens per second in decoding. Furthermore, INT8 quantization on the Ascend 910C preserves accuracy throughout 16 benchmarks, displaying that effectivity enhancements don’t compromise mannequin high quality. 

In conclusion, Huawei CloudMatrix is a next-generation AI datacenter structure designed to beat the scalability limits of standard clusters. Its first manufacturing system, CloudMatrix384, combines 384 Ascend 910C NPUs and 192 Kunpeng CPUs in a completely peer-to-peer supernode related by a high-bandwidth, low-latency Unified Bus. To use this design, the research proposes CloudMatrix-Infer, which separates prefill, decode, and caching into unbiased swimming pools, helps large-scale professional parallelism, and applies hardware-aware optimizations like pipelining and INT8 quantization. Examined on DeepSeek-R1, it achieved superior throughput and latency efficiency in comparison with NVIDIA-based programs, whereas preserving accuracy, showcasing its potential for large-scale AI deployments. 

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