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Comparison

litellm vs TensorRT-LLM

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.

Markdown twin · litellm alternatives · TensorRT-LLM alternatives

GraphCanon updated today

litellm logo

litellm

BerriAI/litellm

★ 53kpushed Jul 11, 2026
vs
TensorRT-LLM logo

TensorRT-LLM

NVIDIA/TensorRT-LLM

★ 14kpushed Jul 11, 2026

Trust & integrity

SignallitellmTensorRT-LLM
Maintenance
Very active (0d since push)
As of today · github_public_v1
Very active (0d since push)
As of today · github_public_v1
Provenance
Not a fork · Organization account
As of today · github_public_v1
Not a fork · Organization account
As of today · github_public_v1
Security (OSV)
2 low (2 low)
As of today · osv@v1
16 low (16 low)
As of today · osv@v1
litellm trust reportTensorRT-LLM trust reportTrust methodology

Tagline

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

Stars

litellm
53k
TensorRT-LLM
14k

Forks

litellm
9.7k
TensorRT-LLM
2.5k

Open issues

litellm
3.9k
TensorRT-LLM
1.5k

Language

litellm
Python
TensorRT-LLM
Python

Adopt for

litellm
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
`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

litellm
-
TensorRT-LLM
-

Runtime

litellm
-
TensorRT-LLM
-

License

litellm
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.
TensorRT-LLM
Other

Last pushed

litellm
Jul 11, 2026
TensorRT-LLM
Jul 11, 2026

Categories

litellm
Inference & Serving, LLM Frameworks
TensorRT-LLM
Inference & Serving, LLM Frameworks

Trust and health

Open issues (now)

litellm
3.9k
TensorRT-LLM
1.5k

Security scan

litellm
2 low (2 low)
TensorRT-LLM
16 low (16 low)

Full report

litellm
Trust report
TensorRT-LLM
Trust report

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

When NOT to use litellm

  • If your project only requires interaction with a single LLM API and basic functionalities, litellm may be overkill.
GitHubDetails

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 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.
GitHubDetails

Explore

litellm trust report →TensorRT-LLM trust report →Inference & Serving category →LLM Frameworks category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: litellm 53k · TensorRT-LLM 14k (synced Jul 11, 2026).

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 and TensorRT-LLM alternatives (litellm markdown twin, TensorRT-LLM markdown twin), 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 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; TensorRT-LLM trust report.