Comparison
ai-serving vs distributed-llama
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.
Markdown twin · ai-serving alternatives · distributed-llama alternatives
GraphCanon updated Sep 20, 2026
17views this month
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Trust & integrity
| Signal | ai-serving | distributed-llama |
|---|---|---|
| Maintenance | Slowing (208d since push) As of Sep 20, 2026 · github_public_v1 | Steady (76d since push) As of Sep 19, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 19, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 15, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · 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
- 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
Stars
- ai-serving
- 166
- distributed-llama
- 3.1k
Forks
- ai-serving
- 31
- distributed-llama
- 250
Open issues
- ai-serving
- 3
- distributed-llama
- 48
Language
- ai-serving
- Scala
- distributed-llama
- C++
Adopt for
- ai-serving
- Ai-Serving is an inference server supporting PMML and ONNX formats via HTTP or gRPC endpoints, easily deployable with Docker.
- distributed-llama
- distributed-llama is a C++ framework that leverages multiple home devices for faster large language model inference, under the MIT license.
Persona
- ai-serving
- -
- distributed-llama
- -
Runtime
- ai-serving
- -
- distributed-llama
- -
License
- ai-serving
- Apache-2.0
- distributed-llama
- MIT
Last pushed
- ai-serving
- Feb 24, 2026
- distributed-llama
- Jul 5, 2026
Categories
- ai-serving
- Inference & Serving
- distributed-llama
- Inference & Serving
Trust and health
Maintenance
- ai-serving
- Slowing (36%)
- distributed-llama
- Steady (60%)
Days since push
- ai-serving
- 208d
- distributed-llama
- 76d
Open issues (now)
- ai-serving
- 3
- distributed-llama
- 48
Stars delta
- ai-serving
- 0 (30d)
- distributed-llama
- +48 (30d)
Owner type
- ai-serving
- Organization
- distributed-llama
- User
Full report
- ai-serving
- Trust report
- distributed-llama
- Trust report
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.
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.
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (autodeployai/ai-serving) · observed Sep 20, 2026
- GitHub forks (autodeployai/ai-serving) · observed Sep 20, 2026
- Last push (autodeployai/ai-serving) · observed Feb 24, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
- GitHub stars (b4rtaz/distributed-llama) · observed Sep 20, 2026
- GitHub forks (b4rtaz/distributed-llama) · observed Sep 20, 2026
- Last push (b4rtaz/distributed-llama) · observed Jul 5, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: ai-serving 166 · distributed-llama 3.1k (synced Sep 20, 2026).
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 and distributed-llama alternatives (ai-serving markdown twin, distributed-llama 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, 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; distributed-llama trust report.