Uploaded January 2026 | Updated September 2026, 2 weeks ago
Disaggregated serving splits model inference into different stages—like compute‑heavy prefill and memory‑heavy decode—and runs them on separate GPU pools. That means every stage gets optimized independently and gets the exact resources, GPU counts, and types it needs. It’s ideal for serving frontier reasoning models at scale where you’re chasing both top performance and cost efficiency.
➡️ Learn more: nvidia.com/en-us/glossary/disaggregated-serving/?ncid=so-yout-332869
📥 Get Started with Dynamo: docs.nvidia.com/dynamo/latest/design_docs/disagg_serving.html?ncid=so-yout-375293
Disaggregated serving splits model inference into different stages—like compute‑heavy prefill and memory‑heavy decode—and runs them on separate GPU pools. That means every stage gets optimized independently and gets the exact resources, GPU counts, and types it needs. It’s ideal for serving frontier reasoning models at scale where you’re chasing both top performance and cost efficiency.
➡️ Learn more: nvidia.com/en-us/glossary/disaggregated-serving/?ncid=so-yout-332869
📥 Get Started with Dynamo: docs.nvidia.com/dynamo/latest/design_docs/disagg_serving.html?ncid=so-yout-375293
