Comparison
atlas vs distributed-llama
Verdict
Pick atlas if focuses on efficient and scalable model deployment with Rust, supporting various GPU technologies and inference frameworks; 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 · atlas alternatives · distributed-llama alternatives
GraphCanon updated Sep 20, 2026
11views this month
vs
Trust & integrity
| Signal | atlas | distributed-llama |
|---|---|---|
| Maintenance | Very active (0d 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 11, 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
- atlas
- Pure Rust Inference Engine
- distributed-llama
- Distributed LLM inference using home devices cluster
Stars
- atlas
- 697
- distributed-llama
- 3.1k
Forks
- atlas
- 105
- distributed-llama
- 250
Open issues
- atlas
- 237
- distributed-llama
- 48
Language
- atlas
- Rust
- distributed-llama
- C++
Adopt for
- atlas
- Focuses on efficient and scalable model deployment with Rust, supporting various GPU technologies and inference frameworks.
- distributed-llama
- distributed-llama is a C++ framework that leverages multiple home devices for faster large language model inference, under the MIT license.
Persona
- atlas
- -
- distributed-llama
- -
Runtime
- atlas
- -
- distributed-llama
- -
License
- atlas
- AGPL-3.0
- distributed-llama
- MIT
Last pushed
- atlas
- Sep 20, 2026
- distributed-llama
- Jul 5, 2026
Categories
- atlas
- Inference & Serving
- distributed-llama
- Inference & Serving
Trust and health
Maintenance
- atlas
- Very active (96%)
- distributed-llama
- Steady (60%)
Days since push
- atlas
- 0d
- distributed-llama
- 76d
Open issues (now)
- atlas
- 237
- distributed-llama
- 48
Stars delta
- atlas
- +87 (30d)
- distributed-llama
- +48 (30d)
Open issues delta
- atlas
- +168 (30d)
- distributed-llama
- 0 (30d)
Owner type
- atlas
- Organization
- distributed-llama
- User
Full report
- atlas
- Trust report
- distributed-llama
- Trust report
Choose atlas if…
- atlas is primarily Rust; distributed-llama is C++.
- License: atlas is AGPL-3.0, distributed-llama is MIT.
- Tags unique to atlas: cuda, dgx, dgx-spark, gb10.
- When aiming for high-performance Rust-based deployment that leverages hardware accelerators like NVIDIA DGX systems and Cuda technology.
When NOT to use atlas
- Avoid if you prefer tools in languages other than Rust for inference engines, since this is purely designed in Rust.
- Not ideal if your deployment environment does not support NVIDIA GPU technologies such as DGX, which are key to maximize performance with Atlas.
Choose distributed-llama if…
- distributed-llama is primarily C++; atlas is Rust.
- License: distributed-llama is MIT, atlas is AGPL-3.0.
- Tags unique to distributed-llama: distributed-computing, 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 (Avarok-Cybersecurity/atlas) · observed Sep 20, 2026
- GitHub forks (Avarok-Cybersecurity/atlas) · observed Sep 20, 2026
- Last push (Avarok-Cybersecurity/atlas) · observed Sep 20, 2026
- License file (AGPL-3.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 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: atlas 697 · distributed-llama 3.1k (synced Sep 20, 2026).
Common questions
- What is the difference between atlas and distributed-llama?
- atlas: Pure Rust Inference Engine. distributed-llama: Distributed LLM inference using home devices cluster. See the comparison table for live GitHub stats and shared categories.
- When should I choose atlas over distributed-llama?
- Choose atlas over distributed-llama when atlas is primarily Rust; distributed-llama is C++; License: atlas is AGPL-3.0, distributed-llama is MIT; Tags unique to atlas: cuda, dgx, dgx-spark, gb10; When aiming for high-performance Rust-based deployment that leverages hardware accelerators like NVIDIA DGX systems and Cuda technology.
- When should I choose distributed-llama over atlas?
- Choose distributed-llama over atlas when distributed-llama is primarily C++; atlas is Rust; License: distributed-llama is MIT, atlas is AGPL-3.0; Tags unique to distributed-llama: distributed-computing, 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 atlas?
- Avoid if you prefer tools in languages other than Rust for inference engines, since this is purely designed in Rust. Not ideal if your deployment environment does not support NVIDIA GPU technologies such as DGX, which are key to maximize performance with Atlas.
- 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 atlas or distributed-llama more popular on GitHub?
- distributed-llama has more GitHub stars (3,060 vs 697). Stars measure visibility, not whether either tool fits your constraints.
- Are atlas and distributed-llama open source?
- Yes - both are open-source projects on GitHub (atlas: AGPL-3.0, distributed-llama: MIT).
- Where can I find alternatives to atlas or distributed-llama?
- GraphCanon lists graph-backed alternatives at atlas alternatives and distributed-llama alternatives (atlas 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, atlas or distributed-llama?
- atlas: Very active. 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 atlas and distributed-llama?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: atlas trust report; distributed-llama trust report.