Comparison
END-TO-END-GENERATIVE-AI-PROJECTS vs lmdeploy
Verdict
Pick END-TO-END-GENERATIVE-AI-PROJECTS if comprehensive generative AI projects focusing on Large Language Models (LLM) frameworks and deployment; 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 · END-TO-END-GENERATIVE-AI-PROJECTS alternatives · lmdeploy alternatives
GraphCanon updated Sep 20, 2026
8views this month
END-TO-END-GENERATIVE-AI-PROJECTS
GURPREETKAURJETHRA/END-TO-END-GENERATIVE-AI-PROJECTS
★ 633pushed Jan 24, 2025
Trust & integrity
| Signal | END-TO-END-GENERATIVE-AI-PROJECTS | lmdeploy |
|---|---|---|
| Maintenance | Dormant (603d since push) As of Sep 20, 2026 · github_public_v1 | Very active (1d since push) As of Sep 7, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 20, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 7, 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
- END-TO-END-GENERATIVE-AI-PROJECTS
- End to End Generative AI Industry Projects on LLM Models with Deployment_Awesome LLM Projects
- lmdeploy
- Toolkit for compressing, deploying, and serving LLMs
Stars
- END-TO-END-GENERATIVE-AI-PROJECTS
- 633
- lmdeploy
- 8.0k
Forks
- END-TO-END-GENERATIVE-AI-PROJECTS
- 182
- lmdeploy
- 737
Open issues
- END-TO-END-GENERATIVE-AI-PROJECTS
- 1
- lmdeploy
- 608
Language
- END-TO-END-GENERATIVE-AI-PROJECTS
- -
- lmdeploy
- Python
Adopt for
- END-TO-END-GENERATIVE-AI-PROJECTS
- Comprehensive generative AI projects focusing on Large Language Models (LLM) frameworks and deployment.
- 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
- END-TO-END-GENERATIVE-AI-PROJECTS
- -
- lmdeploy
- -
Runtime
- END-TO-END-GENERATIVE-AI-PROJECTS
- -
- lmdeploy
- -
License
- END-TO-END-GENERATIVE-AI-PROJECTS
- 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
- END-TO-END-GENERATIVE-AI-PROJECTS
- Jan 24, 2025
- lmdeploy
- Sep 5, 2026
Categories
- END-TO-END-GENERATIVE-AI-PROJECTS
- Inference & Serving, LLM Frameworks, Model Training
- lmdeploy
- Inference & Serving
Trust and health
Maintenance
- END-TO-END-GENERATIVE-AI-PROJECTS
- Dormant (18%)
- lmdeploy
- Very active (96%)
Days since push
- END-TO-END-GENERATIVE-AI-PROJECTS
- 603d
- lmdeploy
- 1d
Open issues (now)
- END-TO-END-GENERATIVE-AI-PROJECTS
- 1
- lmdeploy
- 608
Stars delta
- END-TO-END-GENERATIVE-AI-PROJECTS
- +5 (30d)
- lmdeploy
- +49 (30d)
Open issues delta
- END-TO-END-GENERATIVE-AI-PROJECTS
- 0 (30d)
- lmdeploy
- +1 (30d)
Owner type
- END-TO-END-GENERATIVE-AI-PROJECTS
- User
- lmdeploy
- Organization
Full report
- END-TO-END-GENERATIVE-AI-PROJECTS
- Trust report
- lmdeploy
- Trust report
Choose END-TO-END-GENERATIVE-AI-PROJECTS if…
- License: END-TO-END-GENERATIVE-AI-PROJECTS is MIT, lmdeploy is Apache-2.0.
- Tags unique to END-TO-END-GENERATIVE-AI-PROJECTS: chainlit, finetuning-llms, gemini, generative-ai.
- Also covers LLM Frameworks, Model Training.
- - When you need a wide range of generative AI projects focused on various LLMs such as GPT4o, Gemini, Mistral, and more.
When NOT to use END-TO-END-GENERATIVE-AI-PROJECTS
- - Avoid if your project strictly relies on a single specific framework not covered by this array of projects such as TensorFlow or PyTorch alone.
- - Not advisable for those seeking traditional ML models without an emphasis on generative text and conversational AI capabilities.
Choose lmdeploy if…
- License: lmdeploy is Apache-2.0, END-TO-END-GENERATIVE-AI-PROJECTS 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 (GURPREETKAURJETHRA/END-TO-END-GENERATIVE-AI-PROJECTS) · observed Sep 20, 2026
- GitHub forks (GURPREETKAURJETHRA/END-TO-END-GENERATIVE-AI-PROJECTS) · observed Sep 20, 2026
- Last push (GURPREETKAURJETHRA/END-TO-END-GENERATIVE-AI-PROJECTS) · observed Jan 24, 2025
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- 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 on cards: END-TO-END-GENERATIVE-AI-PROJECTS 633 · lmdeploy 8.0k (synced Sep 20, 2026).
Common questions
- What is the difference between END-TO-END-GENERATIVE-AI-PROJECTS and lmdeploy?
- END-TO-END-GENERATIVE-AI-PROJECTS: End to End Generative AI Industry Projects on LLM Models with Deployment_Awesome LLM Projects. lmdeploy: Toolkit for compressing, deploying, and serving LLMs. See the comparison table for live GitHub stats and shared categories.
- When should I choose END-TO-END-GENERATIVE-AI-PROJECTS over lmdeploy?
- Choose END-TO-END-GENERATIVE-AI-PROJECTS over lmdeploy when License: END-TO-END-GENERATIVE-AI-PROJECTS is MIT, lmdeploy is Apache-2.0; Tags unique to END-TO-END-GENERATIVE-AI-PROJECTS: chainlit, finetuning-llms, gemini, generative-ai; Also covers LLM Frameworks, Model Training; - When you need a wide range of generative AI projects focused on various LLMs such as GPT4o, Gemini, Mistral, and more.
- When should I choose lmdeploy over END-TO-END-GENERATIVE-AI-PROJECTS?
- Choose lmdeploy over END-TO-END-GENERATIVE-AI-PROJECTS when License: lmdeploy is Apache-2.0, END-TO-END-GENERATIVE-AI-PROJECTS 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 END-TO-END-GENERATIVE-AI-PROJECTS?
- - Avoid if your project strictly relies on a single specific framework not covered by this array of projects such as TensorFlow or PyTorch alone. - Not advisable for those seeking traditional ML models without an emphasis on generative text and conversational AI capabilities.
- 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 END-TO-END-GENERATIVE-AI-PROJECTS or lmdeploy more popular on GitHub?
- lmdeploy has more GitHub stars (8,044 vs 633). Stars measure visibility, not whether either tool fits your constraints.
- Are END-TO-END-GENERATIVE-AI-PROJECTS and lmdeploy open source?
- Yes - both are open-source projects on GitHub (END-TO-END-GENERATIVE-AI-PROJECTS: MIT, lmdeploy: Apache-2.0).
- Where can I find alternatives to END-TO-END-GENERATIVE-AI-PROJECTS or lmdeploy?
- GraphCanon lists graph-backed alternatives at END-TO-END-GENERATIVE-AI-PROJECTS alternatives and lmdeploy alternatives (END-TO-END-GENERATIVE-AI-PROJECTS 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, END-TO-END-GENERATIVE-AI-PROJECTS or lmdeploy?
- END-TO-END-GENERATIVE-AI-PROJECTS: Dormant. 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 END-TO-END-GENERATIVE-AI-PROJECTS and lmdeploy?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: END-TO-END-GENERATIVE-AI-PROJECTS trust report; lmdeploy trust report.