--- title: "distributed-llama vs lmdeploy" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/b4rtaz-distributed-llama-vs-internlm-lmdeploy" tools: ["b4rtaz-distributed-llama", "internlm-lmdeploy"] --- # distributed-llama vs lmdeploy *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 lmdeploy if lMDeploy is focused on compressing and efficiently serving LLMs, making it suitable for teams already invested in CUDA environments like Nvidia's GeForce RTX 50 series. [distributed-llama](https://github.com/b4rtaz/distributed-llama) reports 3.0k GitHub stars, 246 forks, and 48 open issues, last pushed Jul 5, 2026. [lmdeploy](https://lmdeploy.readthedocs.io/en/latest) has 8.0k stars, 723 forks, and 607 open issues, last pushed Aug 6, 2026. Figures are from public GitHub metadata via [distributed-llama's repository](https://github.com/b4rtaz/distributed-llama) and [lmdeploy's repository](https://github.com/InternLM/lmdeploy). | | [distributed-llama](/tools/b4rtaz-distributed-llama.md) | [lmdeploy](/tools/internlm-lmdeploy.md) | | --- | --- | --- | | Tagline | Distributed LLM inference using home devices cluster | Toolkit for compressing, deploying, and serving LLMs | | Stars | 3,044 | 7,995 | | Forks | 246 | 723 | | Open issues | 48 | 607 | | Language | C++ | Python | | Adopt for | distributed-llama is a C++ framework that leverages multiple home devices for faster large language model inference, under the MIT license. | LMDeploy is focused on compressing and efficiently serving LLMs, making it suitable for teams already invested in CUDA environments like Nvidia's GeForce RTX 50 series. | | Persona | - | - | | Runtime | - | - | | License | MIT | Licensed under Apache-2.0, enabling flexible use and modification for both commercial and open-source projects, provided that users comply with its terms. | | Categories | Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [distributed-llama](/tools/b4rtaz-distributed-llama.md) | [lmdeploy](/tools/internlm-lmdeploy.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 50d | 1d | | Open issues (now) | 48 | 607 | | 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/internlm-lmdeploy/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: lmdeploy - **Requirements:** Installation is optimized through pip in a Conda environment using Python versions between 3.10 and 3.13. - **Adopt for:** LMDeploy is focused on compressing and efficiently serving LLMs, making it suitable for teams already invested in CUDA environments like Nvidia's GeForce RTX 50 series. - **License detail:** Licensed under Apache-2.0, enabling flexible use and modification for both commercial and open-source projects, provided that users comply with its terms. ## Choose when ### Choose distributed-llama if… - distributed-llama is primarily C++; lmdeploy is Python. - License: distributed-llama is MIT, lmdeploy 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. ### Choose lmdeploy if… - lmdeploy is primarily Python; distributed-llama is C++. - License: lmdeploy is Apache-2.0, distributed-llama is MIT. - Requirements: Installation is optimized through pip in a Conda environment using Python versions between 3.10 and 3.13.. - Tags unique to lmdeploy: codellama, cuda-kernels, deepspeed, fastertransformer. - When your team operates within a CUDA environment, such as using an Nvidia GeForce RTX 50 series GPU, because the default prebuilt wheels are optimized for CUDA 12.8. ## 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 lmdeploy - When your infrastructure relies on software environments or GPUs not aligned with CUDA 12.8, as LMDeploy's default prebuilt wheels might require adjustments to operate optimally. - If you are working exclusively in non-Nvidia GPU ecosystems where LMDeploy's CUDA focus does not align with the hardware optimizations available. ## Common questions ### What is the difference between distributed-llama and lmdeploy? distributed-llama: Distributed LLM inference using home devices cluster. lmdeploy: Toolkit for compressing, deploying, and serving LLMs. See the comparison table for live GitHub stats and shared categories. ### When should I choose distributed-llama over lmdeploy? Choose distributed-llama over lmdeploy when distributed-llama is primarily C++; lmdeploy is Python; License: distributed-llama is MIT, lmdeploy 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 choose lmdeploy over distributed-llama? Choose lmdeploy over distributed-llama when lmdeploy is primarily Python; distributed-llama is C++; License: lmdeploy is Apache-2.0, distributed-llama is MIT; Requirements: Installation is optimized through pip in a Conda environment using Python versions between 3.10 and 3.13.; Tags unique to lmdeploy: codellama, cuda-kernels, deepspeed, fastertransformer; When your team operates within a CUDA environment, such as using an Nvidia GeForce RTX 50 series GPU, because the default prebuilt wheels are optimized for CUDA 12.8. ### 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 lmdeploy? When your infrastructure relies on software environments or GPUs not aligned with CUDA 12.8, as LMDeploy's default prebuilt wheels might require adjustments to operate optimally. If you are working exclusively in non-Nvidia GPU ecosystems where LMDeploy's CUDA focus does not align with the hardware optimizations available. ### Is distributed-llama or lmdeploy more popular on GitHub? lmdeploy has more GitHub stars (7,995 vs 3,044). Stars measure visibility, not whether either tool fits your constraints. ### Are distributed-llama and lmdeploy open source? Yes - both are open-source projects on GitHub (distributed-llama: MIT, lmdeploy: Apache-2.0). ### Where can I find alternatives to distributed-llama or lmdeploy? GraphCanon lists graph-backed alternatives at [distributed-llama alternatives](/tools/b4rtaz-distributed-llama/alternatives) and [lmdeploy alternatives](/tools/internlm-lmdeploy/alternatives) ([distributed-llama markdown twin](/tools/b4rtaz-distributed-llama/alternatives.md), [lmdeploy markdown twin](/tools/internlm-lmdeploy/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-internlm-lmdeploy.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, distributed-llama or lmdeploy? distributed-llama: Steady. lmdeploy: Very active. 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 lmdeploy? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [distributed-llama trust report](/tools/b4rtaz-distributed-llama/trust); [lmdeploy trust report](/tools/internlm-lmdeploy/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/_