Comparison
distributed-llama vs lmdeploy
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.
Markdown twin · distributed-llama alternatives · lmdeploy alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | distributed-llama | lmdeploy |
|---|---|---|
| Maintenance | Active (19d since push) As of 4w · github_public_v1 | Very active (1d 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
- lmdeploy
- Toolkit for compressing, deploying, and serving LLMs
Stars
- distributed-llama
- 3.0k
- lmdeploy
- 8.0k
Forks
- distributed-llama
- 242
- lmdeploy
- 723
Open issues
- distributed-llama
- 48
- lmdeploy
- 607
Language
- distributed-llama
- C++
- lmdeploy
- Python
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.
- lmdeploy
- 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
- distributed-llama
- -
- lmdeploy
- -
Runtime
- distributed-llama
- -
- lmdeploy
- -
License
- distributed-llama
- MIT
- lmdeploy
- Licensed under Apache-2.0, enabling flexible use and modification for both commercial and open-source projects, provided that users comply with its terms.
Last pushed
- distributed-llama
- Jul 5, 2026
- lmdeploy
- Aug 6, 2026
Categories
- distributed-llama
- Inference & Serving
- lmdeploy
- Inference & Serving
Trust and health
Maintenance
- distributed-llama
- Active (82%)
- lmdeploy
- Very active (96%)
Days since push
- distributed-llama
- 19d
- lmdeploy
- 1d
Open issues (now)
- distributed-llama
- 48
- lmdeploy
- 607
Owner type
- distributed-llama
- User
- lmdeploy
- Organization
Full report
- distributed-llama
- Trust report
- lmdeploy
- Trust report
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.
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 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 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.
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 (InternLM/lmdeploy) · observed Aug 7, 2026
- GitHub forks (InternLM/lmdeploy) · observed Aug 7, 2026
- Last push (InternLM/lmdeploy) · observed Aug 6, 2026
- License file (Apache-2.0) · observed Aug 7, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: distributed-llama 3.0k · lmdeploy 8.0k (synced Jul 25, 2026).
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 3,012). 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 and lmdeploy alternatives (distributed-llama markdown twin, lmdeploy 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 lmdeploy?
- distributed-llama: Active. 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; lmdeploy trust report.