Co-Designing AI Models Using Speculative Decoding for Faster LLM Inference
<img width="768" height="432" src="https://developer-blogs.nvidia.com/wp-content/uploads/2026/08/llm-optimize-deploy-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/2026/08/llm-optimize-deploy-768x432.png 768w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/08/llm-optimize-deploy-179x101.png 179w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/08/llm-optimize-deploy-300x169.png 300w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/08/llm-optimize-deploy-625x352.png 625w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/08/llm-optimize-deploy-1536x864.png 1536w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/08/llm-optimize-deploy-645x363.png 645w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/08/llm-optimize-deploy-660x370.png 660w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/08/llm-optimize-deploy-500x281.png 500w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/08/llm-optimize-deploy-160x90.png 160w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/08/llm-optimize-deploy-362x204.png 362w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/08/llm-optimize-deploy-196x110.png 196w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/08/llm-optimize-deploy-1024x576.png 1024w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/08/llm-optimize-deploy-960x540.png 960w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/08/llm-optimize-deploy.webp 1877w" sizes="(max-width: 768px) 100vw, 768px" title="llm-optimize-deploy" />This post is the third in a series on AI model co-design. It explores how to accelerate LLM inference while maintaining accuracy using speculative decoding and...<img width="768" height="432" src="https://developer-blogs.nvidia.com/wp-content/uploads/2026/08/llm-optimize-deploy-768x432.png" 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/2026/08/llm-optimize-deploy-768x432.png 768w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/08/llm-optimize-deploy-179x101.png 179w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/08/llm-optimize-deploy-300x169.png 300w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/08/llm-optimize-deploy-625x352.png 625w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/08/llm-optimize-deploy-1536x864.png 1536w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/08/llm-optimize-deploy-645x363.png 645w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/08/llm-optimize-deploy-660x370.png 660w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/08/llm-optimize-deploy-500x281.png 500w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/08/llm-optimize-deploy-160x90.png 160w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/08/llm-optimize-deploy-362x204.png 362w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/08/llm-optimize-deploy-196x110.png 196w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/08/llm-optimize-deploy-1024x576.png 1024w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/08/llm-optimize-deploy-960x540.png 960w, https://developer-blogs.nvidia.com/wp-content/uploads/2026/08/llm-optimize-deploy.webp 1877w" sizes="auto, (max-width: 768px) 100vw, 768px" title="llm-optimize-deploy" /><p>This post is the third in a series on AI model co-design. It explores how to accelerate LLM inference while maintaining accuracy using speculative decoding and offers five guidelines for selecting draft length and draft mechanism across the Pareto frontier. For a discussion of how model design choices impact both throughput and interactivity without sacrificing accuracy, see AI Model Co…</p>
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