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TopicDisconfirmAI EditorialUpdated 2026-10-18 18:43

英伟达AI工程博客

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英伟达近期通过开发者博客披露了其AI工程化的核心实践,重点展示了在软件构建与模型推理两个层面的技术突破。在软件开发侧,TensorRT团队提出“AI原生”理念,强调通过横向扩展任务、提供结果而非步骤给编码代理,以及实施严格的模型族隔离和对抗性验证来确保代码可靠性;截至2026年7月,该模式已支持128个模型家族在GB300上的测试。在推理部署侧,TensorRT 11.0与Dynamo-Triton 26.07的集成实现了多设备推理,允许单个实例拥有多个GPU并暴露单一gRPC端点。以Cosmos 3 Nano视频生成为例,通过Ulysses上下文并行将延迟从156秒降至34秒。这些案例表明,英伟达正试图通过架构设计与自动化验证的结合,解决AI生成代码的不稳定性及大规模模型服务的复杂性难题。

How it got here

  1. 2026-09-21

    英伟达发布博客介绍TensorRT多设备推理集成,展示Cosmos 3 Nano视频生成延迟从156.6秒降至34.2秒。

    developer.nvidia.com ↗
  2. 2026-09-29

    英伟达TensorRT团队高级工程师Yifei Fang分享AI原生开发经验,阐述如何通过隔离与验证安全利用AI生成代码。

    developer.nvidia.com ↗

What is still unsettled

  • AI原生开发中的“对抗性验证”具体如何量化其对错误级联的阻断效果?
  • 多设备推理在并发吞吐量或成本效益方面的表现是否会在后续版本中得到补充评估?

Sources

  • developer.nvidia.com原始出处:AI原生设计经验
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  • developer.nvidia.com原始出处:TensorRT多设备推理↗
  • Coverage in this topic

    • NVIDIA TensorRT Multi-Device Inference: Abstracting Distributed GPU Acceleration into a Single Model Endpoint2026-10-18News

      An NVIDIA technical blog explains the integration of TensorRT 11.0 and Dynamo-Triton 26.07, allowing a single KIND_MODEL instance to own multiple GPUs and expose one gRPC endpoint. Using Cosmos 3 Nano video generation as an example, it demonstrates reducing latency from 156 seconds to 34 seconds via Ulysses context parallelism, while noting that concurrent throughput and cost efficiency are not covered by this optimization.

    • NVIDIA Engineer Explains AI-Native Software Development: Isolation and Validation in TensorRT2026-10-18News

      Yifei Fang, a senior engineer at NVIDIA TensorRT, shares lessons from building the open-source project TensorRT Model Connect. The article details how to safely leverage AI code generation by selecting horizontally scalable work, providing outcomes instead of recipes to coding agents, and implementing strict model-family isolation and adversarial validation.