--- title: "BodhiApp vs tiny-vllm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/bodhisearch-bodhiapp-vs-jmaczan-tiny-vllm" tools: ["bodhisearch-bodhiapp", "jmaczan-tiny-vllm"] --- # BodhiApp vs tiny-vllm *GraphCanon updated Aug 25, 2026* ## Verdict Pick BodhiApp if bodhiApp streamlines local deployment of open-source and open-weight LLMs via Docker images, compatible with multiple hardware acceleration methods; pick tiny-vllm if for those needing a compact yet potent LLM inference engine built on C++ and CUDA, tiny-vllm presents an accessible framework inspired by its larger sibling, vLLM. [BodhiApp](https://getbodhi.app/) reports 136 GitHub stars, 10 forks, and 10 open issues, last pushed Jul 26, 2026. [tiny-vllm](https://github.com/jmaczan/tiny-vllm) has 1.1k stars, 84 forks, and 0 open issues, last pushed Aug 23, 2026. Figures are from public GitHub metadata via [BodhiApp's repository](https://github.com/BodhiSearch/BodhiApp) and [tiny-vllm's repository](https://github.com/jmaczan/tiny-vllm). | | [BodhiApp](/tools/bodhisearch-bodhiapp.md) | [tiny-vllm](/tools/jmaczan-tiny-vllm.md) | | --- | --- | --- | | Tagline | Run Open Source/Open Weight LLMs locally with OpenAI compatible APIs | Build your own high performance LLM inference engine in C++ and CUDA - a smaller version of vLLM | | Stars | 136 | 1,075 | | Forks | 10 | 84 | | Open issues | 10 | 0 | | Language | TypeScript | C++ | | Adopt for | BodhiApp streamlines local deployment of open-source and open-weight LLMs via Docker images, compatible with multiple hardware acceleration methods. | For those needing a compact yet potent LLM inference engine built on C++ and CUDA, tiny-vllm presents an accessible framework inspired by its larger sibling, vLLM. | | Persona | - | - | | Runtime | - | - | | License | The license information for BodhiApp has not been provided. | Apache-2.0 | | Categories | Inference & Serving, LLM Frameworks | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [BodhiApp](/tools/bodhisearch-bodhiapp.md) | [tiny-vllm](/tools/jmaczan-tiny-vllm.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 18d | 1d | | Open issues (now) | 10 | 0 | | Stars delta | Unknown | +128 (30d) | | Open issues delta | Unknown | -2 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/bodhisearch-bodhiapp/trust.md) | [trust report](/tools/jmaczan-tiny-vllm/trust.md) | ## Decision facts: BodhiApp - **Pricing:** unknown - Pricing details are not mentioned in the repository data. - **Requirements:** Requires Docker; Requires Docker environment. Specific model requirements vary depending on the hardware variant chosen. - **Adopt for:** BodhiApp streamlines local deployment of open-source and open-weight LLMs via Docker images, compatible with multiple hardware acceleration methods. - **License detail:** The license information for BodhiApp has not been provided. ## Decision facts: tiny-vllm - **Adopt for:** For those needing a compact yet potent LLM inference engine built on C++ and CUDA, tiny-vllm presents an accessible framework inspired by its larger sibling, vLLM. ## Choose when ### Choose BodhiApp if… - BodhiApp is primarily TypeScript; tiny-vllm is C++. - Pricing: Pricing details are not mentioned in the repository data.. - Requirements: Requires Docker; Requires Docker environment. Specific model requirements vary depending on the hardware variant chosen.. - Tags unique to BodhiApp: gemma, generative-ai, llama, local-llm. - Also covers LLM Frameworks. - You need to deploy LLMs locally with flexible hardware support including AMD, NVIDIA GPUs, and CPUs. ### Choose tiny-vllm if… - tiny-vllm is primarily C++; BodhiApp is TypeScript. - Tags unique to tiny-vllm: cuda, hpc, lstm. - When you require a lightweight solution for deploying large language model inference in environments with limited resources but still demand high performance. ## When NOT to use BodhiApp - Your project strictly requires non-local deployment options, as BodhiApp focuses on local hosting of models. - If your environment is limited to unsupported GPU hardware or lacks adequate drivers for CUDA, ROCm, or Vulkan acceleration methods. - You need support beyond Mac platforms as BodhiApp does not yet provide installation instructions for other operating systems. ## When NOT to use tiny-vllm - Avoid using tiny-vllm if the application requires the full feature set offered by its larger counterpart, vLLM, as it has been trimmed for lightweight use. - Do not choose this tool when working in environments that do not support CUDA or where a higher abstraction level is preferred over direct C++ and CUDA implementation. ## Common questions ### What is the difference between BodhiApp and tiny-vllm? BodhiApp: Run Open Source/Open Weight LLMs locally with OpenAI compatible APIs. tiny-vllm: Build your own high performance LLM inference engine in C++ and CUDA - a smaller version of vLLM. See the comparison table for live GitHub stats and shared categories. ### When should I choose BodhiApp over tiny-vllm? Choose BodhiApp over tiny-vllm when BodhiApp is primarily TypeScript; tiny-vllm is C++; Pricing: Pricing details are not mentioned in the repository data.; Requirements: Requires Docker; Requires Docker environment. Specific model requirements vary depending on the hardware variant chosen.; Tags unique to BodhiApp: gemma, generative-ai, llama, local-llm; Also covers LLM Frameworks; You need to deploy LLMs locally with flexible hardware support including AMD, NVIDIA GPUs, and CPUs. ### When should I choose tiny-vllm over BodhiApp? Choose tiny-vllm over BodhiApp when tiny-vllm is primarily C++; BodhiApp is TypeScript; Tags unique to tiny-vllm: cuda, hpc, lstm; When you require a lightweight solution for deploying large language model inference in environments with limited resources but still demand high performance. ### When should I avoid BodhiApp? Your project strictly requires non-local deployment options, as BodhiApp focuses on local hosting of models. If your environment is limited to unsupported GPU hardware or lacks adequate drivers for CUDA, ROCm, or Vulkan acceleration methods. You need support beyond Mac platforms as BodhiApp does not yet provide installation instructions for other operating systems. ### When should I avoid tiny-vllm? Avoid using tiny-vllm if the application requires the full feature set offered by its larger counterpart, vLLM, as it has been trimmed for lightweight use. Do not choose this tool when working in environments that do not support CUDA or where a higher abstraction level is preferred over direct C++ and CUDA implementation. ### Is BodhiApp or tiny-vllm more popular on GitHub? tiny-vllm has more GitHub stars (1,075 vs 136). Stars measure visibility, not whether either tool fits your constraints. ### Are BodhiApp and tiny-vllm open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to BodhiApp or tiny-vllm? GraphCanon lists graph-backed alternatives at [BodhiApp alternatives](/tools/bodhisearch-bodhiapp/alternatives) and [tiny-vllm alternatives](/tools/jmaczan-tiny-vllm/alternatives) ([BodhiApp markdown twin](/tools/bodhisearch-bodhiapp/alternatives.md), [tiny-vllm markdown twin](/tools/jmaczan-tiny-vllm/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/bodhisearch-bodhiapp-vs-jmaczan-tiny-vllm.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, BodhiApp or tiny-vllm? BodhiApp: Active. tiny-vllm: 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 BodhiApp and tiny-vllm? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [BodhiApp trust report](/tools/bodhisearch-bodhiapp/trust); [tiny-vllm trust report](/tools/jmaczan-tiny-vllm/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=bodhisearch-bodhiapp`](/api/graphcanon/graph?tool=bodhisearch-bodhiapp) - 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/_