How multi-node inference works for massive LLMs like DeepSeek-R1

Multi-node inference is a technique used to serve large language models like DeepSeek-R1 by recruiting multiple high-performance GPUs to process a single model. This approach overcomes the memory constraints of individual GPU nodes, allowing for production-ready deployment on widely available H100 GPUs. However, it introduces new infrastructure and performance challenges, including ensuring consistent inter-node communication and optimizing model parallelism for efficient inference across multiple GPUs. To overcome these challenges, Baseten has developed production-ready multi-node inference solutions, enabling customers to run mission-critical workloads on scalable, cloud-agnostic infrastructure.

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