Comparison
lmdeploy vs aikit
Verdict
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; pick aikit if aikit is a toolkit designed for fine-tuning, building and deploying large language models (LLMs) with an emphasis on open-source technologies.
Markdown twin · lmdeploy alternatives · aikit alternatives
GraphCanon updated Sep 20, 2026
16views this month
Trust & integrity
| Signal | lmdeploy | aikit |
|---|---|---|
| Maintenance | Very active (1d since push) As of Sep 7, 2026 · github_public_v1 | Very active (0d since push) As of Sep 19, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 7, 2026 · github_public_v1 | Not a fork · Organization 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
- lmdeploy
- Toolkit for compressing, deploying, and serving LLMs
- aikit
- Fine-tune, build, and deploy open-source LLMs easily!
Stars
- lmdeploy
- 8.0k
- aikit
- 539
Forks
- lmdeploy
- 737
- aikit
- 57
Open issues
- lmdeploy
- 608
- aikit
- 37
Language
- lmdeploy
- Python
- aikit
- Go
Adopt for
- 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.
- aikit
- Aikit is a toolkit designed for fine-tuning, building and deploying large language models (LLMs) with an emphasis on open-source technologies.
Persona
- lmdeploy
- -
- aikit
- -
Runtime
- lmdeploy
- -
- aikit
- -
License
- 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.
- aikit
- MIT
Last pushed
- lmdeploy
- Sep 5, 2026
- aikit
- Sep 18, 2026
Categories
- lmdeploy
- Inference & Serving
- aikit
- Inference & Serving, LLM Frameworks, Model Training
Trust and health
Days since push
- lmdeploy
- 1d
- aikit
- 0d
Open issues (now)
- lmdeploy
- 608
- aikit
- 37
Stars delta
- lmdeploy
- +49 (30d)
- aikit
- +5 (30d)
Open issues delta
- lmdeploy
- +1 (30d)
- aikit
- -6 (30d)
Full report
- lmdeploy
- Trust report
- aikit
- Trust report
Choose lmdeploy if…
- lmdeploy is primarily Python; aikit is Go.
- License: lmdeploy is Apache-2.0, aikit 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.
Choose aikit if…
- aikit is primarily Go; lmdeploy is Python.
- License: aikit is MIT, lmdeploy is Apache-2.0.
- Tags unique to aikit: ai, buildkit, chatgpt, docker.
- Also covers LLM Frameworks, Model Training.
- aikit ships Docker support for self-hosted deployment.
- - You need a flexible solution specifically built using Go and prefer its concurrency model.
When NOT to use aikit
- - You have a preference or requirement for Python-based tools due to the lack of native support in Aikit.
- - If your deployment setup strictly uses cloud-specific platforms and you do not use Kubernetes or Docker, as Aikit heavily integrates with containerized environments like these.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (InternLM/lmdeploy) · observed Sep 20, 2026
- GitHub forks (InternLM/lmdeploy) · observed Sep 20, 2026
- Last push (InternLM/lmdeploy) · observed Sep 5, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (kaito-project/aikit) · observed Sep 20, 2026
- GitHub forks (kaito-project/aikit) · observed Sep 20, 2026
- Last push (kaito-project/aikit) · observed Sep 18, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: lmdeploy 8.0k · aikit 539 (synced Sep 20, 2026).
Common questions
- What is the difference between lmdeploy and aikit?
- lmdeploy: Toolkit for compressing, deploying, and serving LLMs. aikit: Fine-tune, build, and deploy open-source LLMs easily!. See the comparison table for live GitHub stats and shared categories.
- When should I choose lmdeploy over aikit?
- Choose lmdeploy over aikit when lmdeploy is primarily Python; aikit is Go; License: lmdeploy is Apache-2.0, aikit 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 choose aikit over lmdeploy?
- Choose aikit over lmdeploy when aikit is primarily Go; lmdeploy is Python; License: aikit is MIT, lmdeploy is Apache-2.0; Tags unique to aikit: ai, buildkit, chatgpt, docker; Also covers LLM Frameworks, Model Training; aikit ships Docker support for self-hosted deployment; - You need a flexible solution specifically built using Go and prefer its concurrency model.
- 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.
- When should I avoid aikit?
- - You have a preference or requirement for Python-based tools due to the lack of native support in Aikit. - If your deployment setup strictly uses cloud-specific platforms and you do not use Kubernetes or Docker, as Aikit heavily integrates with containerized environments like these.
- Is lmdeploy or aikit more popular on GitHub?
- lmdeploy has more GitHub stars (8,044 vs 539). Stars measure visibility, not whether either tool fits your constraints.
- Are lmdeploy and aikit open source?
- Yes - both are open-source projects on GitHub (lmdeploy: Apache-2.0, aikit: MIT).
- Where can I find alternatives to lmdeploy or aikit?
- GraphCanon lists graph-backed alternatives at lmdeploy alternatives and aikit alternatives (lmdeploy markdown twin, aikit 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, lmdeploy or aikit?
- lmdeploy: Very active. aikit: 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 lmdeploy and aikit?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: lmdeploy trust report; aikit trust report.