Comparison
distributed-llama vs BodhiApp
Verdict
Pick distributed-llama if distributed-llama is a C++ framework that leverages multiple home devices for faster large language model inference, under the MIT license; pick BodhiApp if bodhiApp streamlines local deployment of open-source and open-weight LLMs via Docker images, compatible with multiple hardware acceleration methods.
Markdown twin · distributed-llama alternatives · BodhiApp alternatives
GraphCanon updated 1w
vs
Trust & integrity
| Signal | distributed-llama | BodhiApp |
|---|---|---|
| Maintenance | Active (19d since push) As of 4w · github_public_v1 | Active (18d since push) As of 1w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 4w · github_public_v1 | Not a fork · Organization account As of 1w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · 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
- distributed-llama
- Distributed LLM inference using home devices cluster
- BodhiApp
- Run Open Source/Open Weight LLMs locally with OpenAI compatible APIs
Stars
- distributed-llama
- 3.0k
- BodhiApp
- 136
Forks
- distributed-llama
- 242
- BodhiApp
- 10
Open issues
- distributed-llama
- 48
- BodhiApp
- 10
Language
- distributed-llama
- C++
- BodhiApp
- TypeScript
Adopt for
- distributed-llama
- distributed-llama is a C++ framework that leverages multiple home devices for faster large language model inference, under the MIT license.
- BodhiApp
- BodhiApp streamlines local deployment of open-source and open-weight LLMs via Docker images, compatible with multiple hardware acceleration methods.
Persona
- distributed-llama
- -
- BodhiApp
- -
Runtime
- distributed-llama
- -
- BodhiApp
- -
License
- distributed-llama
- MIT
- BodhiApp
- The license information for BodhiApp has not been provided.
Last pushed
- distributed-llama
- Jul 5, 2026
- BodhiApp
- Jul 26, 2026
Categories
- distributed-llama
- Inference & Serving
- BodhiApp
- Inference & Serving, LLM Frameworks
Trust and health
Days since push
- distributed-llama
- 19d
- BodhiApp
- 18d
Open issues (now)
- distributed-llama
- 48
- BodhiApp
- 10
Owner type
- distributed-llama
- User
- BodhiApp
- Organization
Full report
- distributed-llama
- Trust report
- BodhiApp
- Trust report
Choose distributed-llama if…
- distributed-llama is primarily C++; BodhiApp is TypeScript.
- Tags unique to distributed-llama: distributed-computing, llm-inference, neural-network.
- When you have multiple interconnected home devices and want to maximize their combined computing power for LLM inference tasks.
When NOT to use distributed-llama
- For scenarios with fewer than two available devices, as the framework's capability to distribute and boost performance would be limited.
- In professional environments that require strict data privacy controls, due to potential network vulnerabilities among home devices.
Choose BodhiApp if…
- BodhiApp is primarily TypeScript; distributed-llama 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, llm.
- Also covers LLM Frameworks.
- You need to deploy LLMs locally with flexible hardware support including AMD, NVIDIA GPUs, and CPUs.
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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (b4rtaz/distributed-llama) · observed Jul 25, 2026
- GitHub forks (b4rtaz/distributed-llama) · observed Jul 25, 2026
- Last push (b4rtaz/distributed-llama) · observed Jul 5, 2026
- License file (MIT) · observed Jul 25, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (BodhiSearch/BodhiApp) · observed Aug 13, 2026
- GitHub forks (BodhiSearch/BodhiApp) · observed Aug 13, 2026
- Last push (BodhiSearch/BodhiApp) · observed Jul 26, 2026
- License file (unknown) · observed Aug 13, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
GitHub stars on cards: distributed-llama 3.0k · BodhiApp 136 (synced Jul 25, 2026).
Common questions
- What is the difference between distributed-llama and BodhiApp?
- distributed-llama: Distributed LLM inference using home devices cluster. BodhiApp: Run Open Source/Open Weight LLMs locally with OpenAI compatible APIs. See the comparison table for live GitHub stats and shared categories.
- When should I choose distributed-llama over BodhiApp?
- Choose distributed-llama over BodhiApp when distributed-llama is primarily C++; BodhiApp is TypeScript; Tags unique to distributed-llama: distributed-computing, llm-inference, neural-network; When you have multiple interconnected home devices and want to maximize their combined computing power for LLM inference tasks.
- When should I choose BodhiApp over distributed-llama?
- Choose BodhiApp over distributed-llama when BodhiApp is primarily TypeScript; distributed-llama 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, llm; Also covers LLM Frameworks; You need to deploy LLMs locally with flexible hardware support including AMD, NVIDIA GPUs, and CPUs.
- When should I avoid distributed-llama?
- For scenarios with fewer than two available devices, as the framework's capability to distribute and boost performance would be limited. In professional environments that require strict data privacy controls, due to potential network vulnerabilities among home devices.
- 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.
- Is distributed-llama or BodhiApp more popular on GitHub?
- distributed-llama has more GitHub stars (3,012 vs 136). Stars measure visibility, not whether either tool fits your constraints.
- Are distributed-llama and BodhiApp open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to distributed-llama or BodhiApp?
- GraphCanon lists graph-backed alternatives at distributed-llama alternatives and BodhiApp alternatives (distributed-llama markdown twin, BodhiApp 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, distributed-llama or BodhiApp?
- distributed-llama: Active. BodhiApp: 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 distributed-llama and BodhiApp?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: distributed-llama trust report; BodhiApp trust report.