--- title: "distributed-llama vs Forward" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/b4rtaz-distributed-llama-vs-tencent-forward" tools: ["b4rtaz-distributed-llama", "tencent-forward"] --- # distributed-llama vs Forward *GraphCanon updated Aug 24, 2026* ## 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. [distributed-llama](https://github.com/b4rtaz/distributed-llama) reports 3.0k GitHub stars, 246 forks, and 48 open issues, last pushed Jul 5, 2026. [Forward](https://github.com/Tencent/Forward) has 556 stars, 63 forks, and 0 open issues, last pushed Jan 29, 2022. Figures are from public GitHub metadata via [distributed-llama's repository](https://github.com/b4rtaz/distributed-llama) and [Forward's repository](https://github.com/Tencent/Forward). | | [distributed-llama](/tools/b4rtaz-distributed-llama.md) | [Forward](/tools/tencent-forward.md) | | --- | --- | --- | | Tagline | Distributed LLM inference using home devices cluster | A library for high performance deep learning inference on NVIDIA GPUs | | Stars | 3,044 | 556 | | Forks | 246 | 63 | | Open issues | 48 | 0 | | Language | C++ | C++ | | Adopt for | distributed-llama is a C++ framework that leverages multiple home devices for faster large language model inference, under the MIT license. | Forward is an NVIDIA GPU-based high-performance deep learning inference library that converts popular framework models directly into TensorRT for optimized inference. | | Persona | - | - | | Runtime | - | - | | License | MIT | Other license type - specific terms not detailed here; consult repository for details on licensing implications and permissiveness of use and distribution. | | Categories | Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [distributed-llama](/tools/b4rtaz-distributed-llama.md) | [Forward](/tools/tencent-forward.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Dormant (18%) | | Days since push | 50d | 1647d | | Open issues (now) | 48 | 0 | | Stars delta | +32 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Owner type | User | Organization | | Full report | [trust report](/tools/b4rtaz-distributed-llama/trust.md) | [trust report](/tools/tencent-forward/trust.md) | ## 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. ## Decision facts: Forward - **Adopt for:** Forward is an NVIDIA GPU-based high-performance deep learning inference library that converts popular framework models directly into TensorRT for optimized inference. - **License detail:** Other license type - specific terms not detailed here; consult repository for details on licensing implications and permissiveness of use and distribution. ## Choose when ### 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. ### 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 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 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. ## 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 (3,044 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](/tools/b4rtaz-distributed-llama/alternatives) and [Forward alternatives](/tools/tencent-forward/alternatives) ([distributed-llama markdown twin](/tools/b4rtaz-distributed-llama/alternatives.md), [Forward markdown twin](/tools/tencent-forward/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/b4rtaz-distributed-llama-vs-tencent-forward.md) 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: Steady. 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](/tools/b4rtaz-distributed-llama/trust); [Forward trust report](/tools/tencent-forward/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=b4rtaz-distributed-llama`](/api/graphcanon/graph?tool=b4rtaz-distributed-llama) - 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/_