Comparison
TurboLLM vs private-gpt
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,.
Markdown twin · TurboLLM alternatives · private-gpt alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | TurboLLM | private-gpt |
|---|---|---|
| Maintenance | Very active (0d since push) As of 2d · github_public_v1 | Very active (0d since push) As of 2d · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2d · github_public_v1 | Not a fork · Organization account As of 2d · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 2d · osv@v1 | No lockfile (source not queried) As of 6d · 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
- 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
Stars
- TurboLLM
- 171
- private-gpt
- 57k
Forks
- TurboLLM
- 27
- private-gpt
- 7.6k
Open issues
- TurboLLM
- 2
- private-gpt
- 7
Language
- TurboLLM
- TypeScript
- private-gpt
- Python
Adopt for
- TurboLLM
- TurboLLM offers local LLM execution optimized for GPU performance with a polished web UI and APIs compatible with OpenAI/Anthropic.
- private-gpt
- 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
- TurboLLM
- -
- private-gpt
- -
Runtime
- TurboLLM
- -
- private-gpt
- -
License
- TurboLLM
- -
- private-gpt
- Apache-2.0
Last pushed
- TurboLLM
- Jul 15, 2026
- private-gpt
- Jul 14, 2026
Categories
- TurboLLM
- Inference & Serving, Model Training
- private-gpt
- Inference & Serving
Trust and health
Open issues (now)
- TurboLLM
- 2
- private-gpt
- 7
Owner type
- TurboLLM
- User
- private-gpt
- Organization
Full report
- TurboLLM
- Trust report
- private-gpt
- Trust report
Typed relationship
TurboLLM alternative private-gptBoth PrivateGPT and TurboLLM offer solutions to run local LLM engines on auto-tuned GPUs with web UIs, placing them as alternatives for similar needs.
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.
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.
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (mohitsoni48/TurboLLM) · observed Jul 15, 2026
- GitHub forks (mohitsoni48/TurboLLM) · observed Jul 15, 2026
- Last push (mohitsoni48/TurboLLM) · observed Jul 15, 2026
- License file (unknown) · observed Jul 15, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
- GitHub stars (zylon-ai/private-gpt) · observed Jul 15, 2026
- GitHub forks (zylon-ai/private-gpt) · observed Jul 15, 2026
- Last push (zylon-ai/private-gpt) · observed Jul 14, 2026
- License file (Apache-2.0) · observed Jul 15, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: TurboLLM 171 · private-gpt 57k (synced Jul 15, 2026).
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 and private-gpt alternatives (TurboLLM markdown twin, private-gpt 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, 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; private-gpt trust report.