--- title: "lmdeploy vs LLM-Engineers-Handbook" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/internlm-lmdeploy-vs-packtpublishing-llm-engineers-handbook" tools: ["internlm-lmdeploy", "packtpublishing-llm-engineers-handbook"] --- # lmdeploy vs LLM-Engineers-Handbook *GraphCanon updated Jul 12, 2026* ## Verdict Pick lmdeploy when license: lmdeploy is Apache-2.0, LLM-Engineers-Handbook is MIT; pick LLM-Engineers-Handbook when license: LLM-Engineers-Handbook is MIT, lmdeploy is Apache-2.0. [lmdeploy](https://lmdeploy.readthedocs.io/en/latest) reports 8.0k GitHub stars, 703 forks, and 597 open issues, last pushed Jul 10, 2026. [LLM-Engineers-Handbook](https://www.amazon.com/LLM-Engineers-Handbook-engineering-production/dp/1836200072/) has 5.2k stars, 1.2k forks, and 34 open issues, last pushed Apr 22, 2026. Figures are from public GitHub metadata via [lmdeploy's repository](https://github.com/InternLM/lmdeploy) and [LLM-Engineers-Handbook's repository](https://github.com/PacktPublishing/LLM-Engineers-Handbook). | | [lmdeploy](/tools/internlm-lmdeploy.md) | [LLM-Engineers-Handbook](/tools/packtpublishing-llm-engineers-handbook.md) | | --- | --- | --- | | Tagline | LMDeploy is a toolkit for compressing, deploying, and serving LLMs. | LLM's practical guide: From fundamentals to deploying advanced LLM and RAG apps | | Stars | 7,952 | 5,214 | | Forks | 703 | 1,248 | | Open issues | 597 | 34 | | Language | Python | Python | | Adopt for | - | A comprehensive guide for deploying advanced LLM and RAG apps on AWS using LLMOps best practices. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Inference & Serving, LLM Frameworks, Model Training | Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [lmdeploy](/tools/internlm-lmdeploy.md) | [LLM-Engineers-Handbook](/tools/packtpublishing-llm-engineers-handbook.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 0d | 80d | | Open issues (now) | 597 | 34 | | Full report | [trust report](/tools/internlm-lmdeploy/trust.md) | [trust report](/tools/packtpublishing-llm-engineers-handbook/trust.md) | ## Shared compatibility - **Python**: [lmdeploy](/tools/internlm-lmdeploy.md) - Python runtime; [LLM-Engineers-Handbook](/tools/packtpublishing-llm-engineers-handbook.md) - Python runtime ## Decision facts: LLM-Engineers-Handbook - **Pricing:** freemium - The repository itself is free under the MIT license; however, AWS services (like SageMaker and ECR) require paid usage based on your consumption. - **Requirements:** Min 8 GB RAM; Requires Docker; - Requires Docker for managing local infrastructure.; - Python version 3.11 is required; Poetry should already be installed to manage dependencies. - **Adopt for:** A comprehensive guide for deploying advanced LLM and RAG apps on AWS using LLMOps best practices. ## Choose when ### Choose lmdeploy if… - License: lmdeploy is Apache-2.0, LLM-Engineers-Handbook is MIT. - Tags unique to lmdeploy: codellama, cuda-kernels, deepspeed, fastertransformer. - More GitHub stars (8.0k vs 5.2k) - visibility, not fit. ### Choose LLM-Engineers-Handbook if… - License: LLM-Engineers-Handbook is MIT, lmdeploy is Apache-2.0. - Pricing: The repository itself is free under the MIT license; however, AWS services (like SageMaker and ECR) require paid usage based on your consumption.. - Requirements: Min 8 GB RAM; Requires Docker; - Requires Docker for managing local infrastructure.; - Python version 3.11 is required; Poetry should already be installed to manage dependencies.. - Tags unique to LLM-Engineers-Handbook: aws, fine-tuning-llm, genai, llm-evaluation. - Also covers Developer Tools, Evaluation & Observability. - LLM-Engineers-Handbook ships Docker support for self-hosted deployment. - - You are an engineer looking to deploy large language models (LLMs) or retrieval-augmented generation (RAG) applications specifically in an AWS environment. ## When NOT to use lmdeploy - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## When NOT to use LLM-Engineers-Handbook - - If your project is not hosted on AWS, as this tool heavily integrates with AWS services like SageMaker, ECR, and S3, making it less suitable for non-AWS cloud providers. - - You do not want to manage dependencies via Poetry. The guide assumes you are comfortable working within a Poetry-managed environment. ## Common questions ### What is the difference between lmdeploy and LLM-Engineers-Handbook? lmdeploy: LMDeploy is a toolkit for compressing, deploying, and serving LLMs.. LLM-Engineers-Handbook: LLM's practical guide: From fundamentals to deploying advanced LLM and RAG apps. See the comparison table for live GitHub stats and shared categories. ### When should I choose lmdeploy over LLM-Engineers-Handbook? Choose lmdeploy over LLM-Engineers-Handbook when License: lmdeploy is Apache-2.0, LLM-Engineers-Handbook is MIT; Tags unique to lmdeploy: codellama, cuda-kernels, deepspeed, fastertransformer; More GitHub stars (8.0k vs 5.2k) - visibility, not fit. ### When should I choose LLM-Engineers-Handbook over lmdeploy? Choose LLM-Engineers-Handbook over lmdeploy when License: LLM-Engineers-Handbook is MIT, lmdeploy is Apache-2.0; Pricing: The repository itself is free under the MIT license; however, AWS services (like SageMaker and ECR) require paid usage based on your consumption.; Requirements: Min 8 GB RAM; Requires Docker; - Requires Docker for managing local infrastructure.; - Python version 3.11 is required; Poetry should already be installed to manage dependencies.; Tags unique to LLM-Engineers-Handbook: aws, fine-tuning-llm, genai, llm-evaluation; Also covers Developer Tools, Evaluation & Observability; LLM-Engineers-Handbook ships Docker support for self-hosted deployment; - You are an engineer looking to deploy large language models (LLMs) or retrieval-augmented generation (RAG) applications specifically in an AWS environment. ### When should I avoid lmdeploy? Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### When should I avoid LLM-Engineers-Handbook? - If your project is not hosted on AWS, as this tool heavily integrates with AWS services like SageMaker, ECR, and S3, making it less suitable for non-AWS cloud providers. - You do not want to manage dependencies via Poetry. The guide assumes you are comfortable working within a Poetry-managed environment. ### Is lmdeploy or LLM-Engineers-Handbook more popular on GitHub? lmdeploy has more GitHub stars (7,952 vs 5,214). Stars measure visibility, not whether either tool fits your constraints. ### Are lmdeploy and LLM-Engineers-Handbook open source? Yes - both are open-source projects on GitHub (lmdeploy: Apache-2.0, LLM-Engineers-Handbook: MIT). ### Where can I find alternatives to lmdeploy or LLM-Engineers-Handbook? GraphCanon lists graph-backed alternatives at [lmdeploy alternatives](/tools/internlm-lmdeploy/alternatives) and [LLM-Engineers-Handbook alternatives](/tools/packtpublishing-llm-engineers-handbook/alternatives) ([lmdeploy markdown twin](/tools/internlm-lmdeploy/alternatives.md), [LLM-Engineers-Handbook markdown twin](/tools/packtpublishing-llm-engineers-handbook/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-packtpublishing-llm-engineers-handbook.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, lmdeploy or LLM-Engineers-Handbook? lmdeploy: Very active. LLM-Engineers-Handbook: Steady. 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 LLM-Engineers-Handbook? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [lmdeploy trust report](/tools/internlm-lmdeploy/trust); [LLM-Engineers-Handbook trust report](/tools/packtpublishing-llm-engineers-handbook/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/_