Comparison
distributed-llama vs Forward
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 Forward if forward is an NVIDIA GPU-based high-performance deep learning inference library that converts popular framework models directly into TensorRT for optimized inference.
Markdown twin · distributed-llama alternatives · Forward alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | distributed-llama | Forward |
|---|---|---|
| Maintenance | Active (19d since push) As of 4w · github_public_v1 | Dormant (1647d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 4w · github_public_v1 | Not a fork · Organization account As of 2w · 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
- Forward
- A library for high performance deep learning inference on NVIDIA GPUs
Stars
- distributed-llama
- 3.0k
- Forward
- 556
Forks
- distributed-llama
- 242
- Forward
- 63
Open issues
- distributed-llama
- 48
- Forward
- 0
Language
- distributed-llama
- C++
- Forward
- C++
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.
- Forward
- Forward is an NVIDIA GPU-based high-performance deep learning inference library that converts popular framework models directly into TensorRT for optimized inference.
Persona
- distributed-llama
- -
- Forward
- -
Runtime
- distributed-llama
- -
- Forward
- -
License
- distributed-llama
- MIT
- Forward
- Other license type - specific terms not detailed here; consult repository for details on licensing implications and permissiveness of use and distribution.
Last pushed
- distributed-llama
- Jul 5, 2026
- Forward
- Jan 29, 2022
Categories
- distributed-llama
- Inference & Serving
- Forward
- Inference & Serving
Trust and health
Maintenance
- distributed-llama
- Active (82%)
- Forward
- Dormant (18%)
Days since push
- distributed-llama
- 19d
- Forward
- 1647d
Open issues (now)
- distributed-llama
- 48
- Forward
- 0
Owner type
- distributed-llama
- User
- Forward
- Organization
Full report
- distributed-llama
- Trust report
- Forward
- Trust report
Choose distributed-llama if…
- License: distributed-llama is MIT, Forward is Other.
- Tags unique to distributed-llama: distributed-computing, llm-inference.
- 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 Forward if…
- License: Forward is Other, distributed-llama is MIT.
- Tags unique to Forward: cuda, deep-learning, forward, gpu.
- When you need to quickly integrate TensorFlow, PyTorch, Keras, or ONNX models on NVIDIA GPUs for inference and require minimal conversion effort.
When NOT to use Forward
- If your project requires model serving or inference on CPU-only environments, as Forward is optimized for NVIDIA GPUs.
- For users who need extensive customization beyond the supported models (TensorFlow, PyTorch, Keras, ONNX) as expanding support necessitates additional engineering effort.
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 (Tencent/Forward) · observed Aug 4, 2026
- GitHub forks (Tencent/Forward) · observed Aug 4, 2026
- Last push (Tencent/Forward) · observed Jan 29, 2022
- License file (Other) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: distributed-llama 3.0k · Forward 556 (synced Jul 25, 2026).
Common questions
- What is the difference between distributed-llama and Forward?
- distributed-llama: Distributed LLM inference using home devices cluster. Forward: A library for high performance deep learning inference on NVIDIA GPUs. See the comparison table for live GitHub stats and shared categories.
- When should I choose distributed-llama over Forward?
- Choose distributed-llama over Forward when License: distributed-llama is MIT, Forward is Other; Tags unique to distributed-llama: distributed-computing, llm-inference; When you have multiple interconnected home devices and want to maximize their combined computing power for LLM inference tasks.
- When should I choose Forward over distributed-llama?
- Choose Forward over distributed-llama when License: Forward is Other, distributed-llama is MIT; Tags unique to Forward: cuda, deep-learning, forward, gpu; When you need to quickly integrate TensorFlow, PyTorch, Keras, or ONNX models on NVIDIA GPUs for inference and require minimal conversion effort.
- 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 Forward?
- If your project requires model serving or inference on CPU-only environments, as Forward is optimized for NVIDIA GPUs. For users who need extensive customization beyond the supported models (TensorFlow, PyTorch, Keras, ONNX) as expanding support necessitates additional engineering effort.
- Is distributed-llama or Forward more popular on GitHub?
- distributed-llama has more GitHub stars (3,012 vs 556). Stars measure visibility, not whether either tool fits your constraints.
- Are distributed-llama and Forward open source?
- Yes - both are open-source projects on GitHub (distributed-llama: MIT, Forward: Other).
- Where can I find alternatives to distributed-llama or Forward?
- GraphCanon lists graph-backed alternatives at distributed-llama alternatives and Forward alternatives (distributed-llama markdown twin, Forward 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 Forward?
- distributed-llama: Active. Forward: Dormant. 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 Forward?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: distributed-llama trust report; Forward trust report.