Control How Your GPU Shares Work with Green Contexts

<img width="768" height="432" src="https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/image1-3-768x432.png" 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/2026/10/image1-3-768x432.png 768w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/image1-3-179x101.png 179w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/image1-3-300x169.png 300w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/image1-3-625x352.png 625w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/image1-3-645x363.png 645w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/image1-3-660x370.png 660w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/image1-3-500x281.png 500w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/image1-3-160x90.png 160w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/image1-3-362x204.png 362w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/image1-3-196x110.png 196w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/image1-3-960x540.png 960w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/image1-3.webp 1024w" sizes="(max-width: 768px) 100vw, 768px" title="image1" />GPU applications increasingly consist of multiple independent components running at the same time within a single process: a latency-sensitive operator...<img width="768" height="432" src="https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/image1-3-768x432.png" 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/2026/10/image1-3-768x432.png 768w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/image1-3-179x101.png 179w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/image1-3-300x169.png 300w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/image1-3-625x352.png 625w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/image1-3-645x363.png 645w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/image1-3-660x370.png 660w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/image1-3-500x281.png 500w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/image1-3-160x90.png 160w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/image1-3-362x204.png 362w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/image1-3-196x110.png 196w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/image1-3-960x540.png 960w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/10/image1-3.webp 1024w" sizes="(max-width: 768px) 100vw, 768px" title="image1" /><p>GPU applications increasingly consist of multiple independent components running at the same time within a single process: a latency-sensitive operator alongside a throughput-oriented background kernel; a data preprocessing stage alongside model inference; or multiple stages of a processing workflow sharing a single GPU. Controlling how GPU resources are shared between them remains difficult.</p> <p><a href="https://developer.nvidia.com/blog/control-how-your-gpu-shares-work-with-green-contexts/" rel="nofollow" data-wpel-link="internal" target="_self">Source</a></p>