Does Tensorrt-llm or its dependencies require licence for hosting and deploying an open-source llm for commercial purposes

[plsomasundaram93]

Hi Everyone,

I noticed that tensorrt-llm is licensed under Apache 2.0. However, when installing it via pip, it pulls in tensorrt as a dependency, which appears to be licensed under an "other/proprietary" license. There are also several other NVIDIA dependencies with similar "other/proprietary" license designations.

If I install tensorrt-llm on a system with an NVIDIA A10 GPU and use it to serve LLaMA for commercial gains, would I require a license?

Additionally, is there a link or documentation available that explains the terms of the "other/proprietary" license?

I would appreciate any clarification you can provide.

[Advait251206]

Short answer: TensorRT-LLM itself is Apache 2.0, but using it in production does NOT automatically remove NVIDIA proprietary licensing requirements for its dependencies (especially TensorRT). Whether you need an additional license depends on how you obtain and use those proprietary components.

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1. License of TensorRT-LLM

:contentReference[oaicite:0]{index=0} is licensed under:

• ✔ Apache 2.0 (open-source)
• ✔ Allows commercial use, modification, and distribution

So TensorRT-LLM code itself is safe for commercial deployment under Apache 2.0.

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2. The real issue: TensorRT dependency

When you install TensorRT-LLM, it depends on:

:contentReference[oaicite:1]{index=1}

TensorRT is:

• ❌ NOT open source
• ⚠ Licensed under NVIDIA proprietary terms (“other/proprietary” in pip metadata)
• ✔ Free to use in many production scenarios
• ❗ BUT governed by NVIDIA’s EULA

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3. Do you need a separate license for commercial use?

In most cases: No separate paid license is required

If you:

• download TensorRT via official NVIDIA channels
• use supported NVIDIA GPUs (e.g., A10, A100, H100)
• comply with NVIDIA EULA

Then you are typically allowed to:

• ✔ deploy inference workloads commercially
• ✔ serve open-source models (LLaMA, Mistral, etc.)

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4. What the “proprietary” label actually means

The “other/proprietary” classification in pip:

• does NOT mean “you cannot use it commercially”
• it means:
  • source code is not open
  • usage is governed by NVIDIA license terms
  • redistribution is restricted

It is a licensing classification, not a commercial prohibition.

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5. Hosting open-source LLMs (e.g., LLaMA)

If you deploy:

• open-source model (e.g., LLaMA, Mistral, Qwen)
• TensorRT-LLM inference stack
• NVIDIA GPU (A10, etc.)

Then your obligations are split:

You are responsible for:

• model license (Meta / Mistral / Qwen terms)
• data usage compliance
• redistribution rules

NVIDIA provides:

• TensorRT + drivers under proprietary EULA
• allowed for inference deployment under standard terms

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6. Key constraint people miss

Even though:

• TensorRT-LLM = Apache 2.0 ✔

The full runtime stack includes:

• CUDA runtime
• cuBLAS / cuDNN
• TensorRT

All of which are:

• proprietary but freely usable under NVIDIA license terms

So the system is hybrid licensed, not purely open-source.

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7. Practical interpretation for production

You are generally safe to:

✔ Use TensorRT-LLM in production
✔ Serve open-source LLMs commercially
✔ Run on NVIDIA GPUs (A10, etc.)
✔ Monetize inference services

As long as you:

• comply with NVIDIA software EULA
• comply with model license (e.g., LLaMA license restrictions)

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8. Where to check official terms

You should review:

• NVIDIA TensorRT EULA (distributed via NVIDIA Developer website)
• CUDA Toolkit EULA
• TensorRT SDK license terms

These define:

• deployment rights
• redistribution restrictions
• liability terms

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9. Key takeaway

• ✔ TensorRT-LLM is Apache 2.0 → safe for commercial use
• ✔ TensorRT itself is proprietary but generally allowed for production inference
• ❌ No special paid license is typically required just to deploy LLM inference
• ⚠ You must still comply with NVIDIA EULA + model licenses

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If this answer helped or pointed you in the right direction, I'd appreciate it if you could mark it as the accepted answer so it's easier for others with the same issue to find.

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GitHub: https://github.com/Advait251206