--- title: "litellm vs TensorRT-LLM" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/berriai-litellm-vs-nvidia-tensorrt-llm" tools: ["berriai-litellm", "nvidia-tensorrt-llm"] --- # litellm vs TensorRT-LLM *GraphCanon updated Jul 12, 2026* ## Verdict Pick litellm if litellm is a Python SDK and Proxy Server that facilitates the interaction with over 100 LLM APIs, offering features such as cost tracking, guardrails, load balancing, and logging; pick TensorRT-LLM if `TensorRT LLM` is a specialized Python API for optimizing and efficiently running large language models on NVIDIA GPUs, featuring user-friendly interfaces and high-performance optimizations. [litellm](https://docs.litellm.ai/docs/) reports 53k GitHub stars, 9.7k forks, and 3.9k open issues, last pushed Jul 11, 2026. [TensorRT-LLM](https://nvidia.github.io/TensorRT-LLM) has 14k stars, 2.5k forks, and 1.5k open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [litellm's repository](https://github.com/BerriAI/litellm) and [TensorRT-LLM's repository](https://github.com/NVIDIA/TensorRT-LLM). | | [litellm](/tools/berriai-litellm.md) | [TensorRT-LLM](/tools/nvidia-tensorrt-llm.md) | | --- | --- | --- | | Tagline | Python SDK and Proxy Server for calling multiple LLM APIs | Python API for defining and optimizing Large Language Models (LLMs) on NVIDIA GPUs | | Stars | 53,271 | 14,091 | | Forks | 9,671 | 2,547 | | Open issues | 3,915 | 1,500 | | Language | Python | Python | | Adopt for | litellm is a Python SDK and Proxy Server that facilitates the interaction with over 100 LLM APIs, offering features such as cost tracking, guardrails, load balancing, and logging. | `TensorRT LLM` is a specialized Python API for optimizing and efficiently running large language models on NVIDIA GPUs, featuring user-friendly interfaces and high-performance optimizations. | | Persona | - | - | | Runtime | - | - | | License | The licensing terms for LiteLLM are provided under a license type categorized as 'Other'; details of the exact license should be referenced directly from its source. | Other | | Categories | Inference & Serving, LLM Frameworks | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [litellm](/tools/berriai-litellm.md) | [TensorRT-LLM](/tools/nvidia-tensorrt-llm.md) | | --- | --- | --- | | Open issues (now) | 3.9k | 1.5k | | Security scan | 2 low (2 low) | 16 low (16 low) | | Full report | [trust report](/tools/berriai-litellm/trust.md) | [trust report](/tools/nvidia-tensorrt-llm/trust.md) | ## Decision facts: litellm - **Pricing:** freemium - While the core functionality is provided free, specific extended features might require a paid plan. - **Requirements:** Requires Docker - **Adopt for:** litellm is a Python SDK and Proxy Server that facilitates the interaction with over 100 LLM APIs, offering features such as cost tracking, guardrails, load balancing, and logging. - **License detail:** The licensing terms for LiteLLM are provided under a license type categorized as 'Other'; details of the exact license should be referenced directly from its source. ## Decision facts: TensorRT-LLM - **Pricing:** oss - Open source software (OSS) available under a license other than those listed in common OSS categories, implying free use but potentially with restrictions. - **Requirements:** NVIDIA GPU hardware is required for the tool to take full advantage of its optimization capabilities. - **Adopt for:** `TensorRT LLM` is a specialized Python API for optimizing and efficiently running large language models on NVIDIA GPUs, featuring user-friendly interfaces and high-performance optimizations. ## Choose when ### Choose litellm if… - Pricing: While the core functionality is provided free, specific extended features might require a paid plan.. - Requirements: Requires Docker. - Tags unique to litellm: ai-gateway, azure-openai, bedrock, llm. - litellm ships Docker support for self-hosted deployment. - When you need to integrate multiple LLM (Language Learning Modelling) APIs into your application across different providers like Bedrock, Azure, OpenAI, VertexAI, Cohere, Anthropic, Sagemaker, Hugging ### Choose TensorRT-LLM if… - Pricing: Open source software (OSS) available under a license other than those listed in common OSS categories, implying free use but potentially with restrictions.. - Requirements: NVIDIA GPU hardware is required for the tool to take full advantage of its optimization capabilities.. - Tags unique to TensorRT-LLM: blackwell, cuda, llm-serving, moe. - When you are developing or deploying large language models (LLMs) specifically on NVIDIA GPU hardware. ## When NOT to use litellm - If your project only requires interaction with a single LLM API and basic functionalities, litellm may be overkill. ## When NOT to use TensorRT-LLM - When working on CPUs or non-NVIDIA GPUs as the optimizations and hardware support are NVIDIA-specific. - If you prioritize portability across different frameworks over high-performance tuning since TensorRT LLM is tightly integrated with NVIDIA technologies. - For projects that do not require deep level performance optimizations and prefer more general-purpose serving solutions. ## Common questions ### What is the difference between litellm and TensorRT-LLM? litellm: Python SDK and Proxy Server for calling multiple LLM APIs. TensorRT-LLM: Python API for defining and optimizing Large Language Models (LLMs) on NVIDIA GPUs. See the comparison table for live GitHub stats and shared categories. ### When should I choose litellm over TensorRT-LLM? Choose litellm over TensorRT-LLM when Pricing: While the core functionality is provided free, specific extended features might require a paid plan.; Requirements: Requires Docker; Tags unique to litellm: ai-gateway, azure-openai, bedrock, llm; litellm ships Docker support for self-hosted deployment; When you need to integrate multiple LLM (Language Learning Modelling) APIs into your application across different providers like Bedrock, Azure, OpenAI, VertexAI, Cohere, Anthropic, Sagemaker, Hugging. ### When should I choose TensorRT-LLM over litellm? Choose TensorRT-LLM over litellm when Pricing: Open source software (OSS) available under a license other than those listed in common OSS categories, implying free use but potentially with restrictions.; Requirements: NVIDIA GPU hardware is required for the tool to take full advantage of its optimization capabilities.; Tags unique to TensorRT-LLM: blackwell, cuda, llm-serving, moe; When you are developing or deploying large language models (LLMs) specifically on NVIDIA GPU hardware. ### When should I avoid litellm? If your project only requires interaction with a single LLM API and basic functionalities, litellm may be overkill. ### When should I avoid TensorRT-LLM? When working on CPUs or non-NVIDIA GPUs as the optimizations and hardware support are NVIDIA-specific. If you prioritize portability across different frameworks over high-performance tuning since TensorRT LLM is tightly integrated with NVIDIA technologies. For projects that do not require deep level performance optimizations and prefer more general-purpose serving solutions. ### Is litellm or TensorRT-LLM more popular on GitHub? litellm has more GitHub stars (53,271 vs 14,091). Stars measure visibility, not whether either tool fits your constraints. ### Are litellm and TensorRT-LLM open source? Yes - both are open-source projects on GitHub (litellm: Other, TensorRT-LLM: Other). ### Where can I find alternatives to litellm or TensorRT-LLM? GraphCanon lists graph-backed alternatives at [litellm alternatives](/tools/berriai-litellm/alternatives) and [TensorRT-LLM alternatives](/tools/nvidia-tensorrt-llm/alternatives) ([litellm markdown twin](/tools/berriai-litellm/alternatives.md), [TensorRT-LLM markdown twin](/tools/nvidia-tensorrt-llm/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/berriai-litellm-vs-nvidia-tensorrt-llm.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, litellm or TensorRT-LLM? litellm: Very active. TensorRT-LLM: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for litellm and TensorRT-LLM? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [litellm trust report](/tools/berriai-litellm/trust); [TensorRT-LLM trust report](/tools/nvidia-tensorrt-llm/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=berriai-litellm`](/api/graphcanon/graph?tool=berriai-litellm) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_