Already running an inference engine? So where does NVIDIA Dynamo fit in? In five minutes, we break down how Dynamo sits around engines like SGLang, vLLM and TensorRT-LLM to scale inference across GPUs and nodes. Full video in the comments 🔽

NVIDIA Dynamo in 5 Minutes by our very own @Vishakha2394:

@sgl_project @vllm_project Do you like RON? @NVIDIAAI

@sgl_project @vllm_project Hiii, long time no see Shelly! Can we see how is our green lobster mascot doing? 🥺

@sgl_project @vllm_project @nvidia can you make Ron your mascot?

@sgl_project @vllm_project Engines optimize execution; an inference fabric must optimize placement, prefill/decode separation and KV movement across a fleet. Peak tokens/sec can hide queue collapse; useful goodput under bursty workloads is the harder metric. Does Dynamo schedule on per-stage pressure?

@sgl_project @vllm_project the honest answer is that dynamo's architecture isn't about replacing existing engines but optimizing their integration for distributed gpu setups

@sgl_project @vllm_project Love the breakdown 🔥

@sgl_project @vllm_project This separation is important: Inference engine ≠ inference orchestration. As serving expands across GPUs, workers and nodes, routing, cache coordination and resource allocation become a system capability of their own.

@sgl_project @vllm_project so it's an engine for your engine, we've truly built the russian doll of compute

@sgl_project @vllm_project This is great!! The spark is great for all of these tests! On a single GB10 we’re serving DeepSeek Vision-Exp via EXL3 MixedK (~95 GiB) when you don’t have a cluster.

@sgl_project @vllm_project who is $Ron?

The layering is the part that trips people up. Most teams pick an engine first, hit the single node ceiling, then find out that routing and KV cache reuse is a separate problem living above it. Worth saying plainly that this is not a vLLM replacement, because that is where half the confusion lands.

@sgl_project @vllm_project Count words: Dynamo

@sgl_project @vllm_project Layering a scheduler over sglang and vllm introduces distinct queue management overhead between nodes.

@sgl_project @vllm_project Ray Serve usually takes the brunt of multi-node routing for vLLM and TensorRT-LLM setups.

@sgl_project @vllm_project "Already running an inference engine?" YES! 😉❤️ Self-programming AI agents—Open source, licensed under MIT License, running on NVIDIA RTX, in operation, own APP and mesh, no payed APIs, scalable to DGX NVL72 / GB300 Rack, ask your AI! 😘

@sgl_project @vllm_project Scaling inference is only part of the challenge. As AI systems become more distributed, the real question is how we maintain consistency, traceability and reliable outputs across the entire architecture. That’s where frameworks like is relevant

@sgl_project @vllm_project Inference engines are getting commoditized fast, so the real play is owning the layer that stitches them together across the rack.

@sgl_project @vllm_project Who watched the video? Is it worth watching?

@sgl_project @vllm_project Engine vs. orchestration plane is the key distinction. Engines optimize kernels and per-request execution; the surrounding layer coordinates prefill/decode, KV movement, admission control, and multi-node scheduling. Inference at scale is a systems problem.

The engine (vLLM, TensorRT-LLM) is the runtime. Dynamo is the gateway: routing, batching, queueing across nodes. Same mistake people make treating Mule as the system of record instead of the traffic layer. If you already have an engine, Dynamo only pays off once you’re spilling across GPUs, not on a single box.

@sgl_project @vllm_project KV routing, admission controls, etc are table stakes nowadays. Could not live without it.

@sgl_project @vllm_project Dynamo is a good example of infrastructure moving above the engine. Once runtimes become swappable, scheduling, routing and observability across GPUs and nodes become the durable control plane—not the choice of one inference engine.

@sgl_project @vllm_project $ron

@sgl_project @vllm_project $ron

@sgl_project @vllm_project The engine war gets attention, but the production bottleneck is orchestration: routing, KV-cache movement, disaggregated prefill/decode, and keeping GPUs saturated as traffic changes. Dynamo turns a fast engine into a reliable fleet.

@sgl_project @vllm_project Let’s try

@sgl_project @vllm_project people keep asking if dynamo replaces vllm. it sits on top and farms the fleet 🙃

@sgl_project @vllm_project Dynamo这个定位挺有意思，类似推理层的编排调度？正好最近在折腾多卡部署，回头看看视频。

@sgl_project @vllm_project Ohh that makes sense the engine does the model serving and dynamo deals with getting everything coordinated across the gpus

@sgl_project @vllm_project The inference engine is becoming just one component. The real scaling problem is everything around it.

@sgl_project @vllm_project Ron?????

@sgl_project @vllm_project guys i think you should RT this from main acc

@sgl_project @vllm_project Hello, I have sent my CV to Nvidia.