vLLM vs. TensorRT-LLM vs. SGLang: which inference framework fits your stack

Picking an inference framework is one of the few decisions in the stack that's hard to reverse later — it shapes which gateways, orchestration layers, and even which accelerators are realistically available to you downstream. The three frameworks engineers reach for most often — vLLM, TensorRT-LLM, and SGLang — optimize for different things, and the "best" one depends heavily on what layer of the stack you're already committed to.

vLLM: the default for portability

vLLM's PagedAttention scheduler made it the reference implementation for continuous batching, and its broad hardware support (NVIDIA, AMD ROCm, and growing TPU support) makes it the safest choice when you don't want the serving layer to dictate your accelerator choice. The tradeoff is that vLLM's raw throughput on NVIDIA hardware typically trails TensorRT-LLM on the same GPU, because it isn't compiling kernels specifically for the target architecture.

TensorRT-LLM: maximum throughput, NVIDIA-only

TensorRT-LLM trades portability for ceiling — it compiles ahead-of-time, fuses kernels aggressively, and is usually the fastest option on NVIDIA silicon specifically. That's the catch: choosing it is also implicitly choosing NVIDIA at the accelerator hardware layer, since the compiled engines aren't portable. Teams that have already standardized on NVIDIA and are optimizing for cost-per-token at scale get the most out of it.

SGLang: built for agentic and structured workloads

SGLang's RadixAttention shines specifically on workloads with heavy prefix reuse — multi-turn agents, few-shot prompting, and structured generation with constrained decoding. If your workload archetype is agentic rather than single-shot chat completion, SGLang's cache-sharing model often outperforms both alternatives even before raw kernel speed is considered.

How to decide

If you're still choosing your accelerator hardware, start with vLLM and keep your options open. If you've already committed to NVIDIA and throughput-per-dollar is the binding constraint, benchmark TensorRT-LLM against your actual traffic pattern. If your workload is agent-framework-heavy with long, branching conversations, SGLang's cache reuse is worth the switch even mid-project.

Trace any of these through the rest of the stack — gateway, orchestration, and the cloud instances that actually run them — in the interactive graph.