Validating GPU Platforms with Runtime AI Inventory via the NVIDIA DSX AI Factory
<img width="768" height="432" src="https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image1-7-768x432.jpg" class="webfeedsFeaturedVisual wp-post-image" alt="" style="display: block; margin-bottom: 5px; clear:both;max-width: 100%;" link_thumbnail="" decoding="async" srcset="https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image1-7-768x432.jpg 768w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image1-7-179x101.jpg 179w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image1-7-300x169.jpg 300w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image1-7-625x352.jpg 625w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image1-7-1536x864.jpg 1536w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image1-7-645x363.jpg 645w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image1-7-660x370.jpg 660w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image1-7-500x281.jpg 500w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image1-7-160x90.jpg 160w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image1-7-362x204.jpg 362w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image1-7-196x110.jpg 196w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image1-7-1024x576.jpg 1024w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image1-7-960x540.jpg 960w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image1-7.webp 1920w" sizes="(max-width: 768px) 100vw, 768px" title="Best Models Trained on NVIDIA" />As teams deploy and scale AI workloads using NVIDIA Dynamo, Kubeflow, or Slurm, tracking what actually runs requires visibility beyond the platform...<img width="768" height="432" src="https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image1-7-768x432.jpg" class="webfeedsFeaturedVisual wp-post-image" alt="" style="display: block; margin-bottom: 5px; clear:both;max-width: 100%;" link_thumbnail="" decoding="async" loading="lazy" srcset="https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image1-7-768x432.jpg 768w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image1-7-179x101.jpg 179w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image1-7-300x169.jpg 300w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image1-7-625x352.jpg 625w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image1-7-1536x864.jpg 1536w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image1-7-645x363.jpg 645w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image1-7-660x370.jpg 660w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image1-7-500x281.jpg 500w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image1-7-160x90.jpg 160w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image1-7-362x204.jpg 362w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image1-7-196x110.jpg 196w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image1-7-1024x576.jpg 1024w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image1-7-960x540.jpg 960w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/11/image1-7.webp 1920w" sizes="auto, (max-width: 768px) 100vw, 768px" title="Best Models Trained on NVIDIA" /><p>As teams deploy and scale AI workloads using NVIDIA Dynamo, Kubeflow, or Slurm, tracking what actually runs requires visibility beyond the platform configuration captured in a recipe. Application teams define and update these workloads independently of the platform recipe, so a reproducible deployment bundle alone cannot establish which workload artifacts are running on each node.</p>
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