vLLM vs SGLang vs TensorRT-LLM
Choosing an inference engine is one of the highest-leverage decisions in an LLM deployment. The three leading open-source options — vLLM, SGLang, and TensorRT-LLM — all serve the same purpose but make very different tradeoffs. This guide compares them on the dimensions that actually matter in production.
The quick answer#
| vLLM | SGLang | TensorRT-LLM | |
|---|---|---|---|
| Performance | Very good | Very good | Best (on NVIDIA) |
| Ease of use | Easy | Easy | Hard |
| Model support | Broadest | Broad | Selective |
| Hardware | NVIDIA, AMD, TPU+ | NVIDIA, AMD | NVIDIA only |
| Best for | Almost any model, fast iteration | High-concurrency & large MoE | Squeezing max perf from NVIDIA |
If you want one default recommendation: start with vLLM. It has the broadest model and hardware support and the gentlest learning curve. Move to SGLang or TensorRT-LLM when you have a specific performance reason.
What all three have in common#
Before the differences, it's worth knowing that all three modern engines share the same core optimizations out of the box:
- Continuous (in-flight) batching — new requests join the running batch instead of waiting for it to finish
- Paged KV cache — memory-efficient attention key/value storage (PagedAttention and equivalents)
- Prefix caching — reuse of the KV cache for shared prompt prefixes
- Quantization — FP8, INT8, INT4 support
- Speculative decoding — draft-and-verify token generation
- Parallelism — tensor and pipeline parallelism for multi-GPU
So the choice is rarely about features. It's about performance, hardware breadth, and how much engineering effort you want to spend.
vLLM — the default choice#
vLLM has the largest market share and community. Its biggest selling point is breadth: it supports the most model architectures and the widest range of hardware (NVIDIA, AMD, TPU, and more), including multimodal inference.
Use vLLM when you want solid, near-optimal performance for almost any model with minimal setup. It's the safest starting point and the easiest to hire for.
You're serving a standard model, you value fast iteration and broad compatibility over the last 10% of performance, or you're running on non-NVIDIA hardware.
SGLang — high concurrency and large MoE#
SGLang pairs a fast backend runtime with a flexible frontend language for expressing complex generation programs. It has strong support for Chinese open models (DeepSeek, Qwen) and heavy investment in large-scale MoE deployments on systems like GB200 NVL72.
In our own H100 benchmarks (5,980 requests on 2× H100 SXM, TP=2), SGLang is highly competitive with vLLM and pulls ahead on some high-concurrency throughput workloads.
You're running large mixture-of-experts models, need maximum throughput under high concurrency, or want structured/programmatic generation.
TensorRT-LLM — maximum NVIDIA performance#
TensorRT-LLM has the steepest learning curve but usually delivers the best raw performance on NVIDIA hardware. It compiles models into optimized engines with graph-level optimizations and deep hardware integration. At Baseten, it's the most-used engine in production.
The cost is flexibility: it's NVIDIA-only, supports fewer architectures out of the box, and the engine-build step adds operational complexity.
You trade engineering time and flexibility for performance. If you have a stable model, NVIDIA hardware, and performance is your top priority, the investment pays off. If you're still iterating on models, it will slow you down.
A decision framework#
Ask these questions in order:
- Is your hardware non-NVIDIA? → vLLM (or SGLang for AMD).
- Are you running a large MoE model or need peak high-concurrency throughput? → SGLang.
- Is performance the top priority, on stable NVIDIA hardware, with engineering time to invest? → TensorRT-LLM.
- Otherwise → vLLM.
For a personalized recommendation based on your model, scale, and constraints, try the interactive wizard below.
Try it: GPU Selection Advisor →
Get a ranked GPU recommendation to pair with your chosen inference engine.
See the real numbers#
Don't take comparison tables at face value — performance depends heavily on your model, sequence lengths, and concurrency. We ran vLLM and SGLang head-to-head on identical hardware so you can see the tradeoffs on real workloads.
Key Takeaway
There is no universally "fastest" engine. vLLM is the best default; SGLang wins on high-concurrency and large MoE; TensorRT-LLM wins on raw NVIDIA performance when you can afford the setup cost. Benchmark on your workload before committing.
Frequently asked questions
Is vLLM or SGLang faster?
It depends on the workload. In our H100 benchmarks across 5,980 requests, SGLang edges ahead on high-concurrency throughput for some workloads while vLLM is competitive and broader in model support. For raw single-engine peak performance on NVIDIA hardware, TensorRT-LLM usually wins but is harder to set up.
Should I use TensorRT-LLM for production?
Use TensorRT-LLM when you need maximum performance on NVIDIA GPUs and can invest in engine compilation and tuning. For faster iteration, broad model coverage, and easier ops, vLLM or SGLang are usually the better starting point.
Do all three support continuous batching?
Yes. vLLM, SGLang, and TensorRT-LLM all support continuous (in-flight) batching, paged KV cache, quantization, speculative decoding, and prefix caching out of the box. The differences are in performance, hardware breadth, and developer experience.
Which inference engine has the best hardware support?
vLLM has the broadest support (NVIDIA GPUs, AMD, TPU, and more). SGLang supports NVIDIA and AMD. TensorRT-LLM is NVIDIA-only but extracts the most performance from that hardware.
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