--- title: "TurboLLM vs private-gpt" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/mohitsoni48-turbollm-vs-zylon-ai-private-gpt" tools: ["mohitsoni48-turbollm", "zylon-ai-private-gpt"] --- # TurboLLM vs private-gpt *GraphCanon updated Jul 17, 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 private-gpt if privateGPT provides a comprehensive API layer to build private, on-premise AI applications leveraging local OpenAI-compatible inference servers. It offers features such as RAG, skills, tools, text-to-SQL functionalities,. [TurboLLM](https://turbollm.dev) reports 171 GitHub stars, 27 forks, and 2 open issues, last pushed Jul 15, 2026. [private-gpt](https://www.zylon.ai/private-gpt) has 57k stars, 7.6k forks, and 7 open issues, last pushed Jul 14, 2026. Figures are from public GitHub metadata via [TurboLLM's repository](https://github.com/mohitsoni48/TurboLLM) and [private-gpt's repository](https://github.com/zylon-ai/private-gpt). | | [TurboLLM](/tools/mohitsoni48-turbollm.md) | [private-gpt](/tools/zylon-ai-private-gpt.md) | | --- | --- | --- | | Tagline | Run any local LLM engine auto-tuned to your GPU with polished web UI and OpenAI/Anthropic-compatible API | Complete API layer for private AI applications on local models | | Stars | 171 | 57,328 | | Forks | 27 | 7,597 | | Open issues | 2 | 7 | | Language | TypeScript | Python | | Adopt for | TurboLLM offers local LLM execution optimized for GPU performance with a polished web UI and APIs compatible with OpenAI/Anthropic. | PrivateGPT provides a comprehensive API layer to build private, on-premise AI applications leveraging local OpenAI-compatible inference servers. It offers features such as RAG, skills, tools, text-to-SQL functionalities, | | Persona | - | - | | Runtime | - | - | | License | - | Apache-2.0 | | Categories | Inference & Serving, Model Training | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [TurboLLM](/tools/mohitsoni48-turbollm.md) | [private-gpt](/tools/zylon-ai-private-gpt.md) | | --- | --- | --- | | Open issues (now) | 2 | 7 | | Owner type | User | Organization | | Full report | [trust report](/tools/mohitsoni48-turbollm/trust.md) | [trust report](/tools/zylon-ai-private-gpt/trust.md) | **Typed relationship:** TurboLLM _(alternative)_ private-gpt Both PrivateGPT and TurboLLM offer solutions to run local LLM engines on auto-tuned GPUs with web UIs, placing them as alternatives for similar needs. ## 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: private-gpt - **Requirements:** Min 8 GB RAM; Requires Docker - **Adopt for:** PrivateGPT provides a comprehensive API layer to build private, on-premise AI applications leveraging local OpenAI-compatible inference servers. It offers features such as RAG, skills, tools, text-to-SQL functionalities, ## Choose when ### Choose TurboLLM if… - TurboLLM is primarily TypeScript; private-gpt is Python. - Both PrivateGPT and TurboLLM offer solutions to run local LLM engines on auto-tuned GPUs with web UIs, placing them as alternatives for similar needs. - Tags unique to TurboLLM: anthropic-api, claude-code, gpu, inference. - Also covers Model Training. - When you want to self-host an LLM service without external dependencies on Electron or Python. ### Choose private-gpt if… - private-gpt is primarily Python; TurboLLM is TypeScript. - Requirements: Min 8 GB RAM; Requires Docker. - Both PrivateGPT and TurboLLM offer solutions to run local LLM engines on auto-tuned GPUs with web UIs, placing them as alternatives for similar needs. - Tags unique to private-gpt: ai-tools, local-models, mcp, on-premise. - private-gpt ships Docker support for self-hosted deployment. - - You need to deploy and operationalize your own locally-run models without relying on cloud APIs. ## 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 private-gpt - - You prefer simplicity and ease-of-use over full control; PrivateGPT requires more setup than using direct cloud-based AI services. - - Your project does not involve running models locally but strictly relies on public cloud resources for inference server operations. - - You do not have the technical capability to run an OpenAI-compatible inference server or manage local infrastructure effectively. ## Common questions ### What is the difference between TurboLLM and private-gpt? TurboLLM: Run any local LLM engine auto-tuned to your GPU with polished web UI and OpenAI/Anthropic-compatible API. private-gpt: Complete API layer for private AI applications on local models. See the comparison table for live GitHub stats and shared categories. ### When should I choose TurboLLM over private-gpt? Choose TurboLLM over private-gpt when TurboLLM is primarily TypeScript; private-gpt is Python; Both PrivateGPT and TurboLLM offer solutions to run local LLM engines on auto-tuned GPUs with web UIs, placing them as alternatives for similar needs; Tags unique to TurboLLM: anthropic-api, claude-code, gpu, inference; Also covers Model Training; When you want to self-host an LLM service without external dependencies on Electron or Python. ### When should I choose private-gpt over TurboLLM? Choose private-gpt over TurboLLM when private-gpt is primarily Python; TurboLLM is TypeScript; Requirements: Min 8 GB RAM; Requires Docker; Both PrivateGPT and TurboLLM offer solutions to run local LLM engines on auto-tuned GPUs with web UIs, placing them as alternatives for similar needs; Tags unique to private-gpt: ai-tools, local-models, mcp, on-premise; private-gpt ships Docker support for self-hosted deployment; - You need to deploy and operationalize your own locally-run models without relying on cloud APIs. ### 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 private-gpt? - You prefer simplicity and ease-of-use over full control; PrivateGPT requires more setup than using direct cloud-based AI services. - Your project does not involve running models locally but strictly relies on public cloud resources for inference server operations. - You do not have the technical capability to run an OpenAI-compatible inference server or manage local infrastructure effectively. ### Is TurboLLM or private-gpt more popular on GitHub? private-gpt has more GitHub stars (57,328 vs 171). Stars measure visibility, not whether either tool fits your constraints. ### Are TurboLLM and private-gpt open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to TurboLLM or private-gpt? GraphCanon lists graph-backed alternatives at [TurboLLM alternatives](/tools/mohitsoni48-turbollm/alternatives) and [private-gpt alternatives](/tools/zylon-ai-private-gpt/alternatives) ([TurboLLM markdown twin](/tools/mohitsoni48-turbollm/alternatives.md), [private-gpt markdown twin](/tools/zylon-ai-private-gpt/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-zylon-ai-private-gpt.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, TurboLLM or private-gpt? TurboLLM: Very active. private-gpt: 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 TurboLLM and private-gpt? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [TurboLLM trust report](/tools/mohitsoni48-turbollm/trust); [private-gpt trust report](/tools/zylon-ai-private-gpt/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/_