Choosing an Inference Framework: Where vLLM, SGLang, and TensorRT-LLM Each Fit
How the three mainstream inference frameworks differ in latency, throughput, and deployment complexity, and how to pick by workload.
All three frameworks work. Their optimization targets differ completely. Picking wrong will not stop you from running — it will make you optimize the wrong dimension.
Strengths
vLLM: the origin of PagedAttention, broadest ecosystem, widest model support. Good for general workloads, especially when onboarding many models quickly.
SGLang: RadixAttention deeply optimizes prefix reuse, with clear advantages in multi-turn dialogue and structured output. Constrained decoding (JSON schema and similar) is comparatively complete.
TensorRT-LLM: compile-time optimization, pushing operator fusion and kernel tuning to the limit. Lowest latency, but model support lags and the compile pipeline is heavy.
Measured comparison
Same 70B INT4 model on the same 8-GPU machine, fixed workload:
| Metric | vLLM | SGLang | TensorRT-LLM |
|---|---|---|---|
| Time to first token | Medium | Low (with prefix cache) | Lowest |
| High-concurrency throughput | High | Highest | High |
| Model support | Widest | Broad | Limited |
| Deployment complexity | Low | Medium | High |
Numbers shift with model and workload; this only shows relative ordering.
Picking by scenario
- Fast launch, many models: vLLM. Works out of the box, widest model library
- Multi-turn, RAG, structured output: SGLang. Prefix reuse and constrained decoding are real advantages
- Latency-sensitive, fixed model: TensorRT-LLM. Worth the compile time for minimum latency
- Frequent model changes: avoid TensorRT-LLM; compile overhead is prohibitive
An often-missed dimension
Prefix cache hit rate dominates multi-turn scenarios. If the workload is conversational, SGLang’s RadixAttention eliminates computation on repeated prefixes entirely, and the real throughput gain often exceeds the difference between frameworks.
Conversely, for single-turn requests with all-different inputs, prefix caching yields almost nothing, and vLLM’s generality is worth more.
Conclusion
Do not trust a single framework’s benchmark number. Characterize the request pattern first — single or multi-turn, shared prefixes or not, latency- or throughput-first — then choose. All three reach a usable state; the gap only opens in specific workloads.