Comparison
TensorRT-LLM vs ray-llm
Verdict
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 execution; pick ray-llm if ray-LLM is an archived project for deploying large language models on Ray, with its functionalities now integrated into the Ray framework through the ray.serve.llm.
Markdown twin · TensorRT-LLM alternatives · ray-llm alternatives
GraphCanon updated Sep 20, 2026
9views this month
vs
Trust & integrity
| Signal | TensorRT-LLM | ray-llm |
|---|---|---|
| Maintenance | Very active (0d since push) As of Sep 18, 2026 · github_public_v1 | Archived (554d since push) As of Sep 18, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 |
| OSV dependency advisories | Published findings As of Sep 18, 2026 · osv@v1 | No lockfile (source not queried) 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 |
Tagline
- TensorRT-LLM
- Python API for defining and optimizing Large Language Models for efficient inference on NVIDIA GPUs
- ray-llm
- Archived repository for LLMs on Ray, now integrated into Ray.
Stars
- TensorRT-LLM
- 15k
- ray-llm
- 1.3k
Forks
- TensorRT-LLM
- 2.8k
- ray-llm
- 90
Open issues
- TensorRT-LLM
- 1.5k
- ray-llm
- 0
Language
- TensorRT-LLM
- Python
- ray-llm
- -
Adopt for
- 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.
- ray-llm
- Ray-LLM is an archived project for deploying large language models on Ray, with its functionalities now integrated into the Ray framework through the ray.serve.llm and ray.data.llm APIs.
Persona
- TensorRT-LLM
- -
- ray-llm
- -
Runtime
- TensorRT-LLM
- -
- ray-llm
- -
License
- TensorRT-LLM
- Other
- ray-llm
- License information is not provided.
Last pushed
- TensorRT-LLM
- Sep 18, 2026
- ray-llm
- Mar 13, 2025
Categories
- TensorRT-LLM
- Inference & Serving, Model Training
- ray-llm
- Inference & Serving, Model Training
Trust and health
Maintenance
- TensorRT-LLM
- Very active (96%)
- ray-llm
- Archived (8%)
Days since push
- TensorRT-LLM
- 0d
- ray-llm
- 554d
Archived on GitHub
- TensorRT-LLM
- No
- ray-llm
- Yes
Open issues (now)
- TensorRT-LLM
- 1.5k
- ray-llm
- 0
Stars delta
- TensorRT-LLM
- +328 (30d)
- ray-llm
- 0 (30d)
Open issues delta
- TensorRT-LLM
- -106 (30d)
- ray-llm
- 0 (30d)
OSV dependency advisories
- TensorRT-LLM
- Published findings
- ray-llm
- No lockfile (source not queried)
Full report
- TensorRT-LLM
- Trust report
- ray-llm
- Trust report
Choose TensorRT-LLM if…
- Tags unique to TensorRT-LLM: blackwell, cuda, moe, pytorch.
- When you need to optimize Large Language Models for efficient inference on NVIDIA GPUs.
- More GitHub stars (15k vs 1.3k) - visibility, not fit.
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.
Choose ray-llm if…
- Pricing: Pricing details are not available..
- Requirements: Ensure you have the latest version of Ray installed to use the ray.serve.llm and ray.data.llm APIs..
- Tags unique to ray-llm: llm, ray.
- If you are working with Ray and need to deploy large language models, use the ray.serve.llm and ray.data.llm APIs, which are now integrated into the Ray framework.
When NOT to use ray-llm
- Do not use the archived Ray-LLM repository for active development or deployment, as it is no longer maintained and its functionalities have been integrated into the Ray framework.
- If you require the latest features and support for deploying large language models, avoid the archived repository and use the ray.serve.llm and ray.data.llm APIs directly.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (NVIDIA/TensorRT-LLM) · observed Sep 20, 2026
- GitHub forks (NVIDIA/TensorRT-LLM) · observed Sep 20, 2026
- Last push (NVIDIA/TensorRT-LLM) · observed Sep 18, 2026
- License file (Other) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
- GitHub stars (ray-project/ray-llm) · observed Sep 20, 2026
- GitHub forks (ray-project/ray-llm) · observed Sep 20, 2026
- Last push (ray-project/ray-llm) · observed Mar 13, 2025
- License file (unknown) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
GitHub stars on cards: TensorRT-LLM 15k · ray-llm 1.3k (synced Sep 20, 2026).
Common questions
- What is the difference between TensorRT-LLM and ray-llm?
- TensorRT-LLM: Python API for defining and optimizing Large Language Models for efficient inference on NVIDIA GPUs. ray-llm: Archived repository for LLMs on Ray, now integrated into Ray.. See the comparison table for live GitHub stats and shared categories.
- When should I choose TensorRT-LLM over ray-llm?
- Choose TensorRT-LLM over ray-llm when Tags unique to TensorRT-LLM: blackwell, cuda, moe, pytorch; When you need to optimize Large Language Models for efficient inference on NVIDIA GPUs; More GitHub stars (15k vs 1.3k) - visibility, not fit.
- When should I choose ray-llm over TensorRT-LLM?
- Choose ray-llm over TensorRT-LLM when Pricing: Pricing details are not available.; Requirements: Ensure you have the latest version of Ray installed to use the ray.serve.llm and ray.data.llm APIs.; Tags unique to ray-llm: llm, ray; If you are working with Ray and need to deploy large language models, use the ray.serve.llm and ray.data.llm APIs, which are now integrated into the Ray framework.
- 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.
- When should I avoid ray-llm?
- Do not use the archived Ray-LLM repository for active development or deployment, as it is no longer maintained and its functionalities have been integrated into the Ray framework. If you require the latest features and support for deploying large language models, avoid the archived repository and use the ray.serve.llm and ray.data.llm APIs directly.
- Is TensorRT-LLM or ray-llm more popular on GitHub?
- TensorRT-LLM has more GitHub stars (14,645 vs 1,261). Stars measure visibility, not whether either tool fits your constraints.
- Are TensorRT-LLM and ray-llm open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to TensorRT-LLM or ray-llm?
- GraphCanon lists graph-backed alternatives at TensorRT-LLM alternatives and ray-llm alternatives (TensorRT-LLM markdown twin, ray-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, TensorRT-LLM or ray-llm?
- TensorRT-LLM: Very active. ray-llm: Archived. 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 TensorRT-LLM and ray-llm?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: TensorRT-LLM trust report; ray-llm trust report.