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Comparison

lmdeploy vs airllm

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 airllm if airLLM is a notable framework designed specifically for running large language models on low-resource hardware, such as a single 4GB GPU.

Markdown twin · lmdeploy alternatives · airllm alternatives

GraphCanon updated 2w

lmdeploy logo

lmdeploy

InternLM/lmdeploy

★ 8.0kpushed Aug 6, 2026
vs
airllm logo

airllm

lyogavin/airllm

★ 24kpushed Jul 23, 2026

Trust & integrity

Signallmdeployairllm
Maintenance
Very active (1d since push)
As of 2w · github_public_v1
Very active (5d since push)
As of 3w · github_public_v1
Provenance
Not a fork · Organization account
As of 2w · github_public_v1
Not a fork · Personal account
As of 3w · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of 1mo · osv@v1
Published findings
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
lmdeploy trust reportairllm trust reportTrust methodology

Tagline

lmdeploy
Toolkit for compressing, deploying, and serving LLMs
airllm
AirLLM 70B inference with single 4GB GPU

Stars

lmdeploy
8.0k
airllm
24k

Forks

lmdeploy
723
airllm
2.7k

Open issues

lmdeploy
607
airllm
115

Language

lmdeploy
Python
airllm
Jupyter Notebook

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.
airllm
AirLLM is a notable framework designed specifically for running large language models on low-resource hardware, such as a single 4GB GPU.

Persona

lmdeploy
-
airllm
-

Runtime

lmdeploy
-
airllm
-

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.
airllm
Apache-2.0

Last pushed

lmdeploy
Aug 6, 2026
airllm
Jul 23, 2026

Categories

lmdeploy
Inference & Serving
airllm
Inference & Serving

Trust and health

Days since push

lmdeploy
1d
airllm
5d

Open issues (now)

lmdeploy
607
airllm
115

Owner type

lmdeploy
Organization
airllm
User

OSV dependency advisories

lmdeploy
No lockfile (source not queried)
airllm
Published findings

Full report

lmdeploy
Trust report
airllm
Trust report

Shared compatibility

  • Python · lmdeploy: Python runtime · airllm: Python runtime

Choose lmdeploy if…

  • lmdeploy is primarily Python; airllm is Jupyter Notebook.
  • 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.
GitHubDetails

Choose airllm if…

  • airllm is primarily Jupyter Notebook; lmdeploy is Python.
  • Pricing: Free and open-source under the Apache-2.0 license; however, infrastructure costs apply..
  • Requirements: Min 16 GB RAM; A single 4GB GPU is sufficient for using this framework to run large language model inferences..
  • Tags unique to airllm: chinese-llm, chinese-nlp, finetune, generative-ai.
  • If you have limited hardware resources but need to perform inferences on large language models (like the 70B parameter model that AirLLM supports), use AirLLM.

When NOT to use airllm

  • Avoid using AirLLM if you require models to run on higher-end GPUs or multiple GPU clusters, as its strength lies in low-resource efficiency.
  • Do not use AirLLM if you are working primarily with non-Chinese language datasets and models, since support for other languages may be less optimized compared to competition.
GitHubDetails

Explore

lmdeploy trust report →airllm trust report →Inference & Serving category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: lmdeploy 8.0k · airllm 24k (synced Aug 7, 2026).

Common questions

What is the difference between lmdeploy and airllm?
lmdeploy: Toolkit for compressing, deploying, and serving LLMs. airllm: AirLLM 70B inference with single 4GB GPU. See the comparison table for live GitHub stats and shared categories.
When should I choose lmdeploy over airllm?
Choose lmdeploy over airllm when lmdeploy is primarily Python; airllm is Jupyter Notebook; 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 airllm over lmdeploy?
Choose airllm over lmdeploy when airllm is primarily Jupyter Notebook; lmdeploy is Python; Pricing: Free and open-source under the Apache-2.0 license; however, infrastructure costs apply.; Requirements: Min 16 GB RAM; A single 4GB GPU is sufficient for using this framework to run large language model inferences.; Tags unique to airllm: chinese-llm, chinese-nlp, finetune, generative-ai; If you have limited hardware resources but need to perform inferences on large language models (like the 70B parameter model that AirLLM supports), use AirLLM.
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 airllm?
Avoid using AirLLM if you require models to run on higher-end GPUs or multiple GPU clusters, as its strength lies in low-resource efficiency. Do not use AirLLM if you are working primarily with non-Chinese language datasets and models, since support for other languages may be less optimized compared to competition.
Is lmdeploy or airllm more popular on GitHub?
airllm has more GitHub stars (24,183 vs 7,995). Stars measure visibility, not whether either tool fits your constraints.
Are lmdeploy and airllm open source?
Yes - both are open-source projects on GitHub (lmdeploy: Apache-2.0, airllm: Apache-2.0).
Where can I find alternatives to lmdeploy or airllm?
GraphCanon lists graph-backed alternatives at lmdeploy alternatives and airllm alternatives (lmdeploy markdown twin, airllm 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 airllm?
lmdeploy: Very active. airllm: 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 airllm?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: lmdeploy trust report; airllm trust report.

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