How NVIDIA GB200 NVL72 and NVIDIA Dynamo Boost Inference Performance for MoE Models – NVIDIA Technical Blog News and tutorials for developers, data scientists, and IT admins 2025-07-26T02:39:18Z http://www.open-lab.net/blog/feed/ Tiyasa Mitra <![CDATA[How NVIDIA GB200 NVL72 and NVIDIA Dynamo Boost Inference Performance for MoE Models]]> http://www.open-lab.net/blog/?p=101457 2025-06-12T18:48:42Z 2025-06-06T19:00:00Z The latest wave of open source large language models (LLMs), like DeepSeek R1, Llama 4, and Qwen3, have embraced Mixture of Experts (MoE) architectures. Unlike...]]> The latest wave of open source large language models (LLMs), like DeepSeek R1, Llama 4, and Qwen3, have embraced Mixture of Experts (MoE) architectures. Unlike...

The latest wave of open source large language models (LLMs), like DeepSeek R1, Llama 4, and Qwen3, have embraced Mixture of Experts (MoE) architectures. Unlike traditional dense models, MoEs activate only a subset of specialized parameters��known as experts��during inference. This selective activation reduces computational overhead, leading to faster inference times and lower deployment costs.

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