Open-Source AI · Inference server

LMDeploy vs TensorRT-LLM

LMDeploy vs TensorRT-LLM compared for 2026 — features, license, ease of use, performance and which one to choose. Toolkit for compressing and serving LLMs vs Peak throughput on NVIDIA GPUs.

Updated regularly · curated by OpenSourceAI.tech

Choose LMDeploy for teams optimizing quantized serving. Choose TensorRT-LLM for maximum performance on NVIDIA data-center GPUs.

LMDeploy vs TensorRT-LLM at a glance

SpecLMDeployTensorRT-LLM
CategoryInference serverInference server
TypeInference serverInference engine (NVIDIA)
LicenseApache-2.0Apache-2.0
Runs locallySelf-hostedYes
Primary languagePythonC++/Python
Ease of useAdvancedAdvanced
Best forteams optimizing quantized servingmaximum performance on NVIDIA data-center GPUs
GitHub stars8k—

Feature comparison

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

How LMDeploy and TensorRT-LLM score

🏆 Overall edge: TensorRT-LLM — 4.2 vs 3.9 / 5
CriterionLMDeployTensorRT-LLM
Popularity2.5n/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

LMDeploy

Inference server · Apache-2.0

LMDeploy is a toolkit for compressing, quantizing and serving LLMs with high request throughput via its TurboMind engine.

  • High throughput via the TurboMind engine
  • Built-in quantization and compression
  • Efficient KV-cache management
See the LMDeploy 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

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

Which should you choose?

Choose LMDeploy for teams optimizing quantized 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 LMDeploy or TensorRT-LLM easier to use?

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

Are LMDeploy and TensorRT-LLM free?

LMDeploy 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 LMDeploy and TensorRT-LLM locally?

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

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

Choose LMDeploy for teams optimizing quantized serving. Choose TensorRT-LLM for maximum performance on NVIDIA data-center GPUs.

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