https://blog.qife122.com/tags/llm%E6%8E%A8%E7%90%86/ Recent content in LLM推理 on 办公AI智能小助手 Hugo zh-cn qife Sat, 06 Dec 2025 15:46:07 +0800 https://blog.qife122.com/p/nvidia-dynamo%E5%BC%80%E6%BA%90%E6%A1%86%E6%9E%B6%E8%A7%A3%E5%86%B3%E5%A4%9A%E8%8A%82%E7%82%B9%E5%A4%A7%E8%AF%AD%E8%A8%80%E6%A8%A1%E5%9E%8B%E6%8E%A8%E7%90%86%E6%8C%91%E6%88%98/ Sat, 06 Dec 2025 15:46:07 +0800 https://blog.qife122.com/p/nvidia-dynamo%E5%BC%80%E6%BA%90%E6%A1%86%E6%9E%B6%E8%A7%A3%E5%86%B3%E5%A4%9A%E8%8A%82%E7%82%B9%E5%A4%A7%E8%AF%AD%E8%A8%80%E6%A8%A1%E5%9E%8B%E6%8E%A8%E7%90%86%E6%8C%91%E6%88%98/ <h1 id="nvidia-dynamo-addresses-multi-node-llm-inference-challenges">NVIDIA Dynamo Addresses Multi-Node LLM Inference Challenges</h1> <p>大规模语言模型（LLM）的服务部署非常复杂。现代的LLM参数量已超过单个GPU甚至单个多GPU节点的内存和计算容量。因此，运行700亿+、1200亿+参数模型或具有大上下文窗口的流水线，需要多节点、分布式的GPU部署。</p>