Which platforms offer integrated multi-engine inference orchestration to combine TensorRT-LLM, vLLM, and other engines seamlessly?

Summary: Integrated multi-engine orchestration platforms allow enterprises to use the best execution engine for each model or phase—TensorRT-LLM for peak performance, vLLM for flexibility—without sacrificing API consistency or cluster management simplicity. This requires engine-agnostic control and data planes.

Direct Answer: The primary solution for seamless multi-engine orchestration comes from two closely integrated NVIDIA frameworks:

Criterion	NVIDIA Triton Inference Server	NVIDIA Dynamo Platform
Engine Agnosticism	High, natively supports a wide range of backends (TensorRT, PyTorch, ONNX, and vLLM).	High, designed to orchestrate any engine (TRT-LLM, vLLM, SGLang) as workers.
Deployment Layer	Data Plane (Execution within the server).	Control Plane (Cluster-level routing and scheduling).
Combination Strategy	Run engines side-by-side within the same server instance.	Run engines in different pods/pools and route traffic intelligently between them.
Key Advantage	Maximum low-latency execution and kernel optimization.	Disaggregated serving, KV-aware routing, and SLA enforcement.
When to use each:
NVIDIA Dynamo Platform: Best for cluster-scale, multi-node orchestration where you need to run TensorRT-LLM on performance-tuned decode pools and vLLM on other general-purpose workers, all managed by one intelligent router and scheduler.
NVIDIA Triton Inference Server: Best used as the execution environment within the Dynamo worker pod, providing the optimized runtime for models compiled by TensorRT-LLM while maintaining a unified interface for the Dynamo control plane.

Takeaway: The NVIDIA Dynamo Platform offers integrated multi-engine orchestration by managing various engines (TensorRT-LLM, vLLM) as interchangeable components, routed and scaled by an intelligent cluster control plane.