Open-Source AI · Inference server

SGLang vs TensorRT-LLM

SGLang vs TensorRT-LLM compared for 2026 — features, license, ease of use, performance and which one to choose. Fast serving with structured outputs vs Peak throughput on NVIDIA GPUs.

Updated regularly · curated by OpenSourceAI.tech

Choose SGLang for teams needing structured-output serving. Choose TensorRT-LLM for maximum performance on NVIDIA data-center GPUs.

SGLang vs TensorRT-LLM at a glance

SpecSGLangTensorRT-LLM
CategoryInference serverInference server
TypeInference serverInference engine (NVIDIA)
LicenseApache-2.0Apache-2.0
Runs locallySelf-hostedYes
Primary languagePythonC++/Python
Ease of useAdvancedAdvanced
Best forteams needing structured-output servingmaximum performance on NVIDIA data-center GPUs
GitHub stars30.2k—

Feature comparison

FeatureSGLangTensorRT-LLM
OpenAI-compatible API✓✓
Continuous batching✓✓
Quantization✓✓
Multi-GPU✓✓
Structured output✓✓
Docker✓✓

How SGLang and TensorRT-LLM score

🤝 Too close to call — SGLang and TensorRT-LLM land within a hair (4.2 vs 4.2 / 5). Pick on fit, not on score.
CriterionSGLangTensorRT-LLM
Popularity4.0n/a
Maintenance5.0n/a
Ease of use2.52.5
Privacy4.55.0
License freedom5.05.0

Scores are computed automatically from public signals — GitHub stars (popularity), recent commit activity (maintenance), license type (freedom), local-first design (privacy) and onboarding complexity (ease of use). Indicative, not a verdict.

What each one is

SGLang

Inference server · Apache-2.0

SGLang is a fast serving framework for LLMs and vision-language models, featuring RadixAttention and strong support for structured and programmatic generation.

  • Very fast with RadixAttention caching
  • First-class structured / programmatic generation
  • Strong vision-language model support
See the SGLang page →

TensorRT-LLM

Inference engine (NVIDIA) · Apache-2.0

TensorRT-LLM compiles models into highly optimized NVIDIA kernels with in-flight batching, quantization and multi-GPU tensor parallelism — the reference for squeezing maximum tokens per second from NVIDIA hardware.

  • Best-in-class throughput on NVIDIA hardware
  • FP8/INT4 quantization with official support
  • Deep integration with Triton and NVIDIA stack
Visit TensorRT-LLM →

Key differences

SGLang is inference server, while TensorRT-LLM is inference engine (NVIDIA). They also differ in how they run (Self-hosted vs Yes). In short, SGLang fits teams needing structured-output serving, and TensorRT-LLM fits maximum performance on NVIDIA data-center GPUs.

Which should you choose?

Choose SGLang for teams needing structured-output serving. Choose TensorRT-LLM for maximum performance on NVIDIA data-center GPUs.

There is rarely one winner — many setups use both. The right pick depends on your hardware, your team's skills, and whether you value simplicity or control.

Frequently asked questions

Is SGLang or TensorRT-LLM easier to use?

Both sit at a similar level (Advanced). Your choice should come down to fit rather than difficulty.

Are SGLang and TensorRT-LLM free?

SGLang is free and open source (Apache-2.0), and TensorRT-LLM is free and open source (Apache-2.0). Neither charges for the core software.

Can I run SGLang and TensorRT-LLM locally?

SGLang: self-hosted · TensorRT-LLM: yes. Both can be used without sending your data to a third-party cloud where their setup allows.

SGLang vs TensorRT-LLM — which should I pick in 2026?

Choose SGLang for teams needing structured-output serving. Choose TensorRT-LLM for maximum performance on NVIDIA data-center GPUs.

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