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Comparison

lmdeploy vs fastDeploy

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 fastDeploy if fastDeploy simplifies ML/DL model deployment focusing on minimal code for inference pipelines.

Markdown twin · lmdeploy alternatives · fastDeploy alternatives

GraphCanon updated Sep 20, 2026

9views this month

lmdeploy logo

lmdeploy

InternLM/lmdeploy

★ 8.0kpushed Sep 5, 2026
vs
fastDeploy logo

fastDeploy

notAI-tech/fastDeploy

★ 105pushed Feb 10, 2026

Trust & integrity

SignallmdeployfastDeploy
Maintenance
Very active (1d since push)
As of Sep 7, 2026 · github_public_v1
Slowing (221d since push)
As of Sep 20, 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 20, 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 15, 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
lmdeploy trust reportfastDeploy trust reportTrust methodology

Tagline

lmdeploy
Toolkit for compressing, deploying, and serving LLMs
fastDeploy
Deploy DL/ML inference pipelines with minimal extra code.

Stars

lmdeploy
8.0k
fastDeploy
105

Forks

lmdeploy
737
fastDeploy
17

Open issues

lmdeploy
608
fastDeploy
0

Language

lmdeploy
Python
fastDeploy
Python

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.
fastDeploy
fastDeploy simplifies ML/DL model deployment focusing on minimal code for inference pipelines.

Persona

lmdeploy
-
fastDeploy
-

Runtime

lmdeploy
-
fastDeploy
-

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.
fastDeploy
MIT

Last pushed

lmdeploy
Sep 5, 2026
fastDeploy
Feb 10, 2026

Categories

lmdeploy
Inference & Serving
fastDeploy
Inference & Serving

Trust and health

Maintenance

lmdeploy
Very active (96%)
fastDeploy
Slowing (36%)

Days since push

lmdeploy
1d
fastDeploy
221d

Open issues (now)

lmdeploy
608
fastDeploy
0

Stars delta

lmdeploy
+49 (30d)
fastDeploy
0 (30d)

Open issues delta

lmdeploy
+1 (30d)
fastDeploy
0 (30d)

Full report

lmdeploy
Trust report
fastDeploy
Trust report

Choose lmdeploy if…

  • License: lmdeploy is Apache-2.0, fastDeploy 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.
GitHubDetails

Choose fastDeploy if…

  • License: fastDeploy is MIT, lmdeploy is Apache-2.0.
  • Pricing: -.
  • Requirements: - Python is required for running fastDeploy.; - Docker installation is suggested but not mandatory..
  • Tags unique to fastDeploy: deep-learning, docker, falcon, gevent.
  • When you aim to streamline the deployment of TensorFlow Serving, TorchServe, and Triton Inference Server models without extensive coding.

When NOT to use fastDeploy

  • Avoid if you are looking for a solution that supports real-time interactive deployments requiring advanced websocket handling beyond fastDeploy's basic capability.
  • Not recommended when the project requires heavy customization of deployment scripts, as it emphasizes minimal coding and may restrict flexibility in pipeline configurations.
GitHubDetails

Explore

lmdeploy trust report →fastDeploy 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 · fastDeploy 105 (synced Sep 20, 2026).

Common questions

What is the difference between lmdeploy and fastDeploy?
lmdeploy: Toolkit for compressing, deploying, and serving LLMs. fastDeploy: Deploy DL/ML inference pipelines with minimal extra code.. See the comparison table for live GitHub stats and shared categories.
When should I choose lmdeploy over fastDeploy?
Choose lmdeploy over fastDeploy when License: lmdeploy is Apache-2.0, fastDeploy 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 fastDeploy over lmdeploy?
Choose fastDeploy over lmdeploy when License: fastDeploy is MIT, lmdeploy is Apache-2.0; Pricing: -; Requirements: - Python is required for running fastDeploy.; - Docker installation is suggested but not mandatory.; Tags unique to fastDeploy: deep-learning, docker, falcon, gevent; When you aim to streamline the deployment of TensorFlow Serving, TorchServe, and Triton Inference Server models without extensive coding.
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 fastDeploy?
Avoid if you are looking for a solution that supports real-time interactive deployments requiring advanced websocket handling beyond fastDeploy's basic capability. Not recommended when the project requires heavy customization of deployment scripts, as it emphasizes minimal coding and may restrict flexibility in pipeline configurations.
Is lmdeploy or fastDeploy more popular on GitHub?
lmdeploy has more GitHub stars (8,044 vs 105). Stars measure visibility, not whether either tool fits your constraints.
Are lmdeploy and fastDeploy open source?
Yes - both are open-source projects on GitHub (lmdeploy: Apache-2.0, fastDeploy: MIT).
Where can I find alternatives to lmdeploy or fastDeploy?
GraphCanon lists graph-backed alternatives at lmdeploy alternatives and fastDeploy alternatives (lmdeploy markdown twin, fastDeploy 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 fastDeploy?
lmdeploy: Very active. fastDeploy: Slowing. 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 fastDeploy?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: lmdeploy trust report; fastDeploy trust report.

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