--- title: "lmdeploy vs fastDeploy" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/internlm-lmdeploy-vs-notai-tech-fastdeploy" tools: ["internlm-lmdeploy", "notai-tech-fastdeploy"] --- # lmdeploy vs fastDeploy *GraphCanon updated Sep 20, 2026* ## 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. [lmdeploy](https://lmdeploy.readthedocs.io/en/latest) reports 8.0k GitHub stars, 737 forks, and 608 open issues, last pushed Sep 5, 2026. [fastDeploy](https://github.com/notAI-tech/fastDeploy) has 105 stars, 17 forks, and 0 open issues, last pushed Feb 10, 2026. Figures are from public GitHub metadata via [lmdeploy's repository](https://github.com/InternLM/lmdeploy) and [fastDeploy's repository](https://github.com/notAI-tech/fastDeploy). | | [lmdeploy](/tools/internlm-lmdeploy.md) | [fastDeploy](/tools/notai-tech-fastdeploy.md) | | --- | --- | --- | | Tagline | Toolkit for compressing, deploying, and serving LLMs | Deploy DL/ML inference pipelines with minimal extra code. | | Stars | 8,044 | 105 | | Forks | 737 | 17 | | Open issues | 608 | 0 | | Language | Python | Python | | Adopt for | 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 simplifies ML/DL model deployment focusing on minimal code for inference pipelines. | | Persona | - | - | | Runtime | - | - | | License | Licensed under Apache-2.0, enabling flexible use and modification for both commercial and open-source projects, provided that users comply with its terms. | MIT | | Categories | Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [lmdeploy](/tools/internlm-lmdeploy.md) | [fastDeploy](/tools/notai-tech-fastdeploy.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 1d | 221d | | Open issues (now) | 608 | 0 | | Stars delta | +49 (30d) | 0 (30d) | | Open issues delta | +1 (30d) | 0 (30d) | | Full report | [trust report](/tools/internlm-lmdeploy/trust.md) | [trust report](/tools/notai-tech-fastdeploy/trust.md) | ## Decision facts: lmdeploy - **Requirements:** Installation is optimized through pip in a Conda environment using Python versions between 3.10 and 3.13. - **Adopt for:** 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. - **License detail:** Licensed under Apache-2.0, enabling flexible use and modification for both commercial and open-source projects, provided that users comply with its terms. ## Decision facts: fastDeploy - **Pricing:** freemium - - - **Requirements:** - Python is required for running fastDeploy.; - Docker installation is suggested but not mandatory. - **Adopt for:** fastDeploy simplifies ML/DL model deployment focusing on minimal code for inference pipelines. ## Choose when ### 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. ### 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 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 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. ## 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](/tools/internlm-lmdeploy/alternatives) and [fastDeploy alternatives](/tools/notai-tech-fastdeploy/alternatives) ([lmdeploy markdown twin](/tools/internlm-lmdeploy/alternatives.md), [fastDeploy markdown twin](/tools/notai-tech-fastdeploy/alternatives.md)), 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](/compare/internlm-lmdeploy-vs-notai-tech-fastdeploy.md) 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](/tools/internlm-lmdeploy/trust); [fastDeploy trust report](/tools/notai-tech-fastdeploy/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=internlm-lmdeploy`](/api/graphcanon/graph?tool=internlm-lmdeploy) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_