--- title: "TurboLLM vs ray-llm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/mohitsoni48-turbollm-vs-ray-project-ray-llm" tools: ["mohitsoni48-turbollm", "ray-project-ray-llm"] --- # TurboLLM vs ray-llm *GraphCanon updated Aug 13, 2026* ## Verdict Pick TurboLLM if turboLLM offers local LLM execution optimized for GPU performance with a polished web UI and APIs compatible with OpenAI/Anthropic; pick ray-llm if archived LLM deployment tool integrated into Ray; now focus on built-in APIs (`ray.serve.llm` & `ray.data.llm`). [TurboLLM](https://turbollm.dev) reports 225 GitHub stars, 36 forks, and 6 open issues, last pushed Aug 11, 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 [TurboLLM's repository](https://github.com/mohitsoni48/TurboLLM) and [ray-llm's repository](https://github.com/ray-project/ray-llm). | | [TurboLLM](/tools/mohitsoni48-turbollm.md) | [ray-llm](/tools/ray-project-ray-llm.md) | | --- | --- | --- | | Tagline | Run any local LLM engine auto-tuned to your GPU with polished web UI and OpenAI/Anthropic-compatible API | Archived repository; LLM serving APIs integrated into the Ray project | | Stars | 225 | 1,261 | | Forks | 36 | 90 | | Open issues | 6 | 0 | | Language | TypeScript | - | | Adopt for | TurboLLM offers local LLM execution optimized for GPU performance with a polished web UI and APIs compatible with OpenAI/Anthropic. | Archived LLM deployment tool integrated into Ray; now focus on built-in APIs (`ray.serve.llm` & `ray.data.llm`). | | Persona | - | - | | Runtime | - | - | | License | - | - | | Categories | Inference & Serving, Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [TurboLLM](/tools/mohitsoni48-turbollm.md) | [ray-llm](/tools/ray-project-ray-llm.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Archived (8%) | | Days since push | 1d | 507d | | Archived on GitHub | No | Yes | | Open issues (now) | 6 | 0 | | Owner type | User | Organization | | Full report | [trust report](/tools/mohitsoni48-turbollm/trust.md) | [trust report](/tools/ray-project-ray-llm/trust.md) | ## Decision facts: TurboLLM - **Adopt for:** TurboLLM offers local LLM execution optimized for GPU performance with a polished web UI and APIs compatible with OpenAI/Anthropic. ## Decision facts: ray-llm - **Adopt for:** Archived LLM deployment tool integrated into Ray; now focus on built-in APIs (`ray.serve.llm` & `ray.data.llm`). ## Choose when ### Choose TurboLLM if… - Tags unique to TurboLLM: ai, anthropic-api, claude-code, gpu. - When you want to self-host an LLM service without external dependencies on Electron or Python. - More recently updated (last pushed Aug 11, 2026). ### Choose ray-llm if… - Tags unique to ray-llm: llm-serving, ray. - For deploying LLMs with new Ray-integrated APIs, ensuring direct support and updates from the Ray team. - More GitHub stars (1.3k vs 225) - visibility, not fit. ## When NOT to use TurboLLM - If your setup does not include a GPU as TurboLLM primarily optimizes performance specifically for that hardware. - When you require heavy model training capabilities on the same platform; TurboLLM focuses more on running and inference tasks with LLMs. ## When NOT to use ray-llm - If seeking a standalone solution distinct from Ray’s main project, preferring specialized tools. - For needs requiring active maintenance and development in this specific repository; consider alternative up-to-date projects. ## Common questions ### What is the difference between TurboLLM and ray-llm? TurboLLM: Run any local LLM engine auto-tuned to your GPU with polished web UI and OpenAI/Anthropic-compatible API. ray-llm: Archived repository; LLM serving APIs integrated into the Ray project. See the comparison table for live GitHub stats and shared categories. ### When should I choose TurboLLM over ray-llm? Choose TurboLLM over ray-llm when Tags unique to TurboLLM: ai, anthropic-api, claude-code, gpu; When you want to self-host an LLM service without external dependencies on Electron or Python; More recently updated (last pushed Aug 11, 2026). ### When should I choose ray-llm over TurboLLM? Choose ray-llm over TurboLLM when Tags unique to ray-llm: llm-serving, ray; For deploying LLMs with new Ray-integrated APIs, ensuring direct support and updates from the Ray team; More GitHub stars (1.3k vs 225) - visibility, not fit. ### When should I avoid TurboLLM? If your setup does not include a GPU as TurboLLM primarily optimizes performance specifically for that hardware. When you require heavy model training capabilities on the same platform; TurboLLM focuses more on running and inference tasks with LLMs. ### When should I avoid ray-llm? If seeking a standalone solution distinct from Ray’s main project, preferring specialized tools. For needs requiring active maintenance and development in this specific repository; consider alternative up-to-date projects. ### Is TurboLLM or ray-llm more popular on GitHub? ray-llm has more GitHub stars (1,261 vs 225). Stars measure visibility, not whether either tool fits your constraints. ### Are TurboLLM and ray-llm open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to TurboLLM or ray-llm? GraphCanon lists graph-backed alternatives at [TurboLLM alternatives](/tools/mohitsoni48-turbollm/alternatives) and [ray-llm alternatives](/tools/ray-project-ray-llm/alternatives) ([TurboLLM markdown twin](/tools/mohitsoni48-turbollm/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/mohitsoni48-turbollm-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, TurboLLM or ray-llm? TurboLLM: 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 TurboLLM and ray-llm? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [TurboLLM trust report](/tools/mohitsoni48-turbollm/trust); [ray-llm trust report](/tools/ray-project-ray-llm/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=mohitsoni48-turbollm`](/api/graphcanon/graph?tool=mohitsoni48-turbollm) - 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/_