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Comparison

rtp-llm vs TensorRT-LLM

Verdict

Pick rtp-llm if rTP-LLM is Alibaba's high-performance inference engine for LLMs, specifically designed and optimized with CUDA. It supports a variety of applications from GPT to LLaMA models; pick TensorRT-LLM if tensorRT-LLM is a Python API for defining and optimizing Large Language Models for efficient inference on NVIDIA GPUs, offering components for creating Python and C++ runtimes for performant inference.

Markdown twin · rtp-llm alternatives · TensorRT-LLM alternatives

GraphCanon updated Sep 20, 2026

21views this month

rtp-llm logo

rtp-llm

alibaba/rtp-llm

★ 1.3kpushed Sep 20, 2026
vs
TensorRT-LLM logo

TensorRT-LLM

NVIDIA/TensorRT-LLM

★ 15kpushed Sep 18, 2026

Trust & integrity

Signalrtp-llmTensorRT-LLM
Maintenance
Very active (0d since push)
As of Sep 20, 2026 · github_public_v1
Very active (0d since push)
As of Sep 18, 2026 · github_public_v1
Provenance
Not a fork · Organization account
As of Sep 20, 2026 · github_public_v1
Not a fork · Organization account
As of Sep 18, 2026 · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of Jul 11, 2026 · osv@v1
Published findings
As of Sep 18, 2026 · osv@v1
deps.dev advisories
Not queried
deps.dev@v1
Not queried
deps.dev@v1
OpenSSF Scorecard
Not queried
openssf-scorecard@v1
Not queried
openssf-scorecard@v1
rtp-llm trust reportTensorRT-LLM trust reportTrust methodology

Tagline

rtp-llm
Alibaba's high-performance LLM inference engine for diverse applications.
TensorRT-LLM
Python API for defining and optimizing Large Language Models for efficient inference on NVIDIA GPUs

Stars

rtp-llm
1.3k
TensorRT-LLM
15k

Forks

rtp-llm
275
TensorRT-LLM
2.8k

Open issues

rtp-llm
218
TensorRT-LLM
1.5k

Language

rtp-llm
Python
TensorRT-LLM
Python

Adopt for

rtp-llm
RTP-LLM is Alibaba's high-performance inference engine for LLMs, specifically designed and optimized with CUDA. It supports a variety of applications from GPT to LLaMA models.
TensorRT-LLM
TensorRT-LLM is a Python API for defining and optimizing Large Language Models for efficient inference on NVIDIA GPUs, offering components for creating Python and C++ runtimes for performant inference execution.

Persona

rtp-llm
-
TensorRT-LLM
-

Runtime

rtp-llm
-
TensorRT-LLM
-

License

rtp-llm
The tool operates under the Apache-2.0 license making it freely available for commercial use, provided proper attribution is given.
TensorRT-LLM
Other

Last pushed

rtp-llm
Sep 20, 2026
TensorRT-LLM
Sep 18, 2026

Categories

rtp-llm
Inference & Serving
TensorRT-LLM
Inference & Serving, Model Training

Trust and health

Open issues (now)

rtp-llm
218
TensorRT-LLM
1.5k

Stars delta

rtp-llm
+29 (30d)
TensorRT-LLM
+328 (30d)

Open issues delta

rtp-llm
+24 (30d)
TensorRT-LLM
-106 (30d)

OSV dependency advisories

rtp-llm
No lockfile (source not queried)
TensorRT-LLM
Published findings

Full report

rtp-llm
Trust report
TensorRT-LLM
Trust report

Choose rtp-llm if…

  • License: rtp-llm is Apache-2.0, TensorRT-LLM is Other.
  • Requirements: Requires CUDA configuration and NVIDIA GPU availability to exploit its full performance capabilities..
  • Tags unique to rtp-llm: gpt, inference, llama, llm.
  • When you are looking for a tool that leverages CUDA-based optimization for deploying Large Language Models (LLMs) across diverse applications.

When NOT to use rtp-llm

  • When your development environment does not support CUDA as RTP-LLM is primarily based on it and might perform inadequately without direct GPU-acceleration from NVIDIA.
  • If your project has strict licensing constraints; while the Apache-2.0 license is permissive, certain projects may require tools with different or more restrictive licenses to meet compliance needs.
  • For applications that do not align well with Alibaba’s backend setup and prefer a more independent or competitor-based solution for their LLM serving needs.
GitHubDetails

Choose TensorRT-LLM if…

  • License: TensorRT-LLM is Other, rtp-llm is Apache-2.0.
  • Tags unique to TensorRT-LLM: blackwell, cuda, moe, pytorch.
  • Also covers Model Training.
  • When you need to optimize Large Language Models for efficient inference on NVIDIA GPUs.

When NOT to use TensorRT-LLM

  • If your hardware does not include NVIDIA GPUs, as TensorRT-LLM is specifically optimized for NVIDIA GPUs.
  • If you do not require a Python API for defining and optimizing LLMs, and prefer a different interface or tool.
  • When you are working in a non-NVIDIA GPU environment, as TensorRT-LLM's optimizations are tailored to NVIDIA hardware.
GitHubDetails

Explore

rtp-llm trust report →TensorRT-LLM trust report →Inference & Serving category →Model Training 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: rtp-llm 1.3k · TensorRT-LLM 15k (synced Sep 20, 2026).

Common questions

What is the difference between rtp-llm and TensorRT-LLM?
rtp-llm: Alibaba's high-performance LLM inference engine for diverse applications.. TensorRT-LLM: Python API for defining and optimizing Large Language Models for efficient inference on NVIDIA GPUs. See the comparison table for live GitHub stats and shared categories.
When should I choose rtp-llm over TensorRT-LLM?
Choose rtp-llm over TensorRT-LLM when License: rtp-llm is Apache-2.0, TensorRT-LLM is Other; Requirements: Requires CUDA configuration and NVIDIA GPU availability to exploit its full performance capabilities.; Tags unique to rtp-llm: gpt, inference, llama, llm; When you are looking for a tool that leverages CUDA-based optimization for deploying Large Language Models (LLMs) across diverse applications.
When should I choose TensorRT-LLM over rtp-llm?
Choose TensorRT-LLM over rtp-llm when License: TensorRT-LLM is Other, rtp-llm is Apache-2.0; Tags unique to TensorRT-LLM: blackwell, cuda, moe, pytorch; Also covers Model Training; When you need to optimize Large Language Models for efficient inference on NVIDIA GPUs.
When should I avoid rtp-llm?
When your development environment does not support CUDA as RTP-LLM is primarily based on it and might perform inadequately without direct GPU-acceleration from NVIDIA. If your project has strict licensing constraints; while the Apache-2.0 license is permissive, certain projects may require tools with different or more restrictive licenses to meet compliance needs. For applications that do not align well with Alibaba’s backend setup and prefer a more independent or competitor-based solution for their LLM serving needs.
When should I avoid TensorRT-LLM?
If your hardware does not include NVIDIA GPUs, as TensorRT-LLM is specifically optimized for NVIDIA GPUs. If you do not require a Python API for defining and optimizing LLMs, and prefer a different interface or tool. When you are working in a non-NVIDIA GPU environment, as TensorRT-LLM's optimizations are tailored to NVIDIA hardware.
Is rtp-llm or TensorRT-LLM more popular on GitHub?
TensorRT-LLM has more GitHub stars (14,645 vs 1,341). Stars measure visibility, not whether either tool fits your constraints.
Are rtp-llm and TensorRT-LLM open source?
Yes - both are open-source projects on GitHub (rtp-llm: Apache-2.0, TensorRT-LLM: Other).
Where can I find alternatives to rtp-llm or TensorRT-LLM?
GraphCanon lists graph-backed alternatives at rtp-llm alternatives and TensorRT-LLM alternatives (rtp-llm 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, rtp-llm or TensorRT-LLM?
rtp-llm: 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 rtp-llm and TensorRT-LLM?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: rtp-llm trust report; TensorRT-LLM trust report.

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