agentsop-llm-engine-selection

Comparing vLLM vs TensorRT-LLM requires evaluating your constraint vector, including hardware compatibility and workload type. TensorRT-LLM excels on NVIDIA hardware with optimized topologies, while vLLM offers broader hardware support and easier deployment migration across different environments.

For structured output and agent workloads, SGLang is often recommended due to its specialized runtime handling of complex prompt structures. Selecting the correct engine involves mapping these workload requirements to the serving stack's specific strengths.

To migrate your LLM deployment, apply a structured decision workflow using hard elimination filters and topology sanity checks. This ensures the new serving stack resolves your current operational constraints and validates the migration path before execution.

You should not use vLLM for LLM inference when your workload hits hard elimination filters like edge deployment constraints or specific structured-output requirements. In these cases, runtimes like llama.cpp, Ollama, or MLX may be better suited for your environment.

The best way to deploy LLM inference on edge devices is by filtering engines through hardware constraints and topology sanity checks. Lightweight runtimes like llama.cpp, Ollama, or MLX are typically recommended over heavier serving stacks like TGI or vLLM.