--- title: "ai-serving vs distributed-llama" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/autodeployai-ai-serving-vs-b4rtaz-distributed-llama" tools: ["autodeployai-ai-serving", "b4rtaz-distributed-llama"] --- # ai-serving vs distributed-llama *GraphCanon updated Sep 20, 2026* ## Verdict Pick ai-serving if ai-Serving is an inference server supporting PMML and ONNX formats via HTTP or gRPC endpoints, easily deployable with Docker; 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. [ai-serving](https://github.com/autodeployai/ai-serving) reports 166 GitHub stars, 31 forks, and 3 open issues, last pushed Feb 24, 2026. [distributed-llama](https://github.com/b4rtaz/distributed-llama) has 3.1k stars, 250 forks, and 48 open issues, last pushed Jul 5, 2026. Figures are from public GitHub metadata via [ai-serving's repository](https://github.com/autodeployai/ai-serving) and [distributed-llama's repository](https://github.com/b4rtaz/distributed-llama). | | [ai-serving](/tools/autodeployai-ai-serving.md) | [distributed-llama](/tools/b4rtaz-distributed-llama.md) | | --- | --- | --- | | Tagline | Serving AI/ML models in open standard formats PMML and ONNX with HTTP and gRPC endpoints | Distributed LLM inference using home devices cluster | | Stars | 166 | 3,060 | | Forks | 31 | 250 | | Open issues | 3 | 48 | | Language | Scala | C++ | | Adopt for | Ai-Serving is an inference server supporting PMML and ONNX formats via HTTP or gRPC endpoints, easily deployable with Docker. | distributed-llama is a C++ framework that leverages multiple home devices for faster large language model inference, under the MIT license. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ai-serving](/tools/autodeployai-ai-serving.md) | [distributed-llama](/tools/b4rtaz-distributed-llama.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Steady (60%) | | Days since push | 208d | 76d | | Open issues (now) | 3 | 48 | | Stars delta | 0 (30d) | +48 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/autodeployai-ai-serving/trust.md) | [trust report](/tools/b4rtaz-distributed-llama/trust.md) | ## Decision facts: ai-serving - **Adopt for:** Ai-Serving is an inference server supporting PMML and ONNX formats via HTTP or gRPC endpoints, easily deployable with Docker. ## Decision facts: distributed-llama - **Adopt for:** distributed-llama is a C++ framework that leverages multiple home devices for faster large language model inference, under the MIT license. ## Choose when ### Choose ai-serving if… - ai-serving is primarily Scala; distributed-llama is C++. - License: ai-serving is Apache-2.0, distributed-llama is MIT. - Tags unique to ai-serving: ai-serving, grpc, inference-server, onnx. - When you need to serve models in both PMML and ONNX formats without manual configuration changes between formats. ### Choose distributed-llama if… - distributed-llama is primarily C++; ai-serving is Scala. - License: distributed-llama is MIT, ai-serving is Apache-2.0. - 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 ai-serving - Avoid if your team lacks familiarity or willingness to use Scala for deployment through sbt build system for customization needs. - Not suitable when only one model format, either PMML or ONNX but not both, is needed and a simpler solution would suffice. - If your project strictly requires a non-Dockerized setup that does not align with using pre-built Docker images. ## 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. ## Common questions ### What is the difference between ai-serving and distributed-llama? ai-serving: Serving AI/ML models in open standard formats PMML and ONNX with HTTP and gRPC endpoints. distributed-llama: Distributed LLM inference using home devices cluster. See the comparison table for live GitHub stats and shared categories. ### When should I choose ai-serving over distributed-llama? Choose ai-serving over distributed-llama when ai-serving is primarily Scala; distributed-llama is C++; License: ai-serving is Apache-2.0, distributed-llama is MIT; Tags unique to ai-serving: ai-serving, grpc, inference-server, onnx; When you need to serve models in both PMML and ONNX formats without manual configuration changes between formats. ### When should I choose distributed-llama over ai-serving? Choose distributed-llama over ai-serving when distributed-llama is primarily C++; ai-serving is Scala; License: distributed-llama is MIT, ai-serving is Apache-2.0; 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 avoid ai-serving? Avoid if your team lacks familiarity or willingness to use Scala for deployment through sbt build system for customization needs. Not suitable when only one model format, either PMML or ONNX but not both, is needed and a simpler solution would suffice. If your project strictly requires a non-Dockerized setup that does not align with using pre-built Docker images. ### 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. ### Is ai-serving or distributed-llama more popular on GitHub? distributed-llama has more GitHub stars (3,060 vs 166). Stars measure visibility, not whether either tool fits your constraints. ### Are ai-serving and distributed-llama open source? Yes - both are open-source projects on GitHub (ai-serving: Apache-2.0, distributed-llama: MIT). ### Where can I find alternatives to ai-serving or distributed-llama? GraphCanon lists graph-backed alternatives at [ai-serving alternatives](/tools/autodeployai-ai-serving/alternatives) and [distributed-llama alternatives](/tools/b4rtaz-distributed-llama/alternatives) ([ai-serving markdown twin](/tools/autodeployai-ai-serving/alternatives.md), [distributed-llama markdown twin](/tools/b4rtaz-distributed-llama/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/autodeployai-ai-serving-vs-b4rtaz-distributed-llama.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ai-serving or distributed-llama? ai-serving: Slowing. distributed-llama: Steady. 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 ai-serving and distributed-llama? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ai-serving trust report](/tools/autodeployai-ai-serving/trust); [distributed-llama trust report](/tools/b4rtaz-distributed-llama/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=autodeployai-ai-serving`](/api/graphcanon/graph?tool=autodeployai-ai-serving) - 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/_