---
title: "TensorRT-LLM vs ray-llm"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/nvidia-tensorrt-llm-vs-ray-project-ray-llm"
tools: ["nvidia-tensorrt-llm", "ray-project-ray-llm"]
---

# TensorRT-LLM vs ray-llm

*GraphCanon updated Sep 20, 2026*

## 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.

[TensorRT-LLM](https://nvidia.github.io/TensorRT-LLM) reports 15k GitHub stars, 2.8k forks, and 1.5k open issues, last pushed Sep 18, 2026. [ray-llm](https://docs.ray.io/en/latest/) has 1.3k stars, 90 forks, and 0 open issues, last pushed Mar 13, 2025. Figures are from public GitHub metadata via [TensorRT-LLM's repository](https://github.com/NVIDIA/TensorRT-LLM) and [ray-llm's repository](https://github.com/ray-project/ray-llm).

| | [TensorRT-LLM](/tools/nvidia-tensorrt-llm.md) | [ray-llm](/tools/ray-project-ray-llm.md) |
| --- | --- | --- |
| Tagline | Python API for defining and optimizing Large Language Models for efficient inference on NVIDIA GPUs | Archived repository for LLMs on Ray, now integrated into Ray. |
| Stars | 14,645 | 1,261 |
| Forks | 2,756 | 90 |
| Open issues | 1,510 | 0 |
| Language | Python | - |
| Adopt for | 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 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 | - | - |
| Runtime | - | - |
| License | Other | License information is not provided. |
| Categories | Inference & Serving, Model Training | Inference & Serving, Model Training |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [TensorRT-LLM](/tools/nvidia-tensorrt-llm.md) | [ray-llm](/tools/ray-project-ray-llm.md) |
| --- | --- | --- |
| Maintenance | Very active (96%) | Archived (8%) |
| Days since push | 0d | 554d |
| Archived on GitHub | No | Yes |
| Open issues (now) | 1.5k | 0 |
| Stars delta | +328 (30d) | 0 (30d) |
| Open issues delta | -106 (30d) | 0 (30d) |
| Full report | [trust report](/tools/nvidia-tensorrt-llm/trust.md) | [trust report](/tools/ray-project-ray-llm/trust.md) |

## Decision facts: TensorRT-LLM

- **Adopt for:** 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.

## Decision facts: ray-llm

- **Pricing:** unknown - 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.
- **Adopt for:** 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.
- **License detail:** License information is not provided.

## Choose when

### 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.

### 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 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 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.

## 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](/tools/nvidia-tensorrt-llm/alternatives) and [ray-llm alternatives](/tools/ray-project-ray-llm/alternatives) ([TensorRT-LLM markdown twin](/tools/nvidia-tensorrt-llm/alternatives.md), [ray-llm markdown twin](/tools/ray-project-ray-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/nvidia-tensorrt-llm-vs-ray-project-ray-llm.md) 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](/tools/nvidia-tensorrt-llm/trust); [ray-llm trust report](/tools/ray-project-ray-llm/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=nvidia-tensorrt-llm`](/api/graphcanon/graph?tool=nvidia-tensorrt-llm)
- 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/_