--- title: "kserve vs LLMs-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kserve-kserve-vs-rasbt-llms-from-scratch" tools: ["kserve-kserve", "rasbt-llms-from-scratch"] --- # kserve vs LLMs-from-scratch *GraphCanon updated Jul 11, 2026* ## Verdict Pick kserve when kserve is primarily Go; LLMs-from-scratch is Jupyter Notebook; pick LLMs-from-scratch when lLMs-from-scratch is primarily Jupyter Notebook; kserve is Go. [kserve](https://kserve.github.io/website/) reports 5.7k GitHub stars, 1.6k forks, and 555 open issues, last pushed Jul 10, 2026. [LLMs-from-scratch](https://amzn.to/4fqvn0D) has 99k stars, 15k forks, and 4 open issues, last pushed Jun 2, 2026. Figures are from public GitHub metadata via [kserve's repository](https://github.com/kserve/kserve) and [LLMs-from-scratch's repository](https://github.com/rasbt/LLMs-from-scratch). | | [kserve](/tools/kserve-kserve.md) | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) | | --- | --- | --- | | Tagline | Standardized Distributed Generative and Predictive AI Inference Platform for Scalable, Multi-Framework Deployment on Kubernetes | Implement a ChatGPT-like LLM in PyTorch from scratch, step by step | | Stars | 5,674 | 98,899 | | Forks | 1,566 | 15,183 | | Open issues | 555 | 4 | | Language | Go | Jupyter Notebook | | Adopt for | - | LLMs-from-scratch is a project-oriented repository aimed at building PyTorch-based language models from the ground up, with detailed step-by-step instructions. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Other | | Categories | Inference & Serving, LLM Frameworks, Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [kserve](/tools/kserve-kserve.md) | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 0d | 38d | | Open issues (now) | 555 | 4 | | Owner type | Organization | User | | Full report | [trust report](/tools/kserve-kserve/trust.md) | [trust report](/tools/rasbt-llms-from-scratch/trust.md) | ## Decision facts: LLMs-from-scratch - **Adopt for:** LLMs-from-scratch is a project-oriented repository aimed at building PyTorch-based language models from the ground up, with detailed step-by-step instructions. ## Choose when ### Choose kserve if… - kserve is primarily Go; LLMs-from-scratch is Jupyter Notebook. - License: kserve is Apache-2.0, LLMs-from-scratch is Other. - Tags unique to kserve: cncf, genai, hacktoberfest, istio. - Also covers Inference & Serving. ### Choose LLMs-from-scratch if… - LLMs-from-scratch is primarily Jupyter Notebook; kserve is Go. - License: LLMs-from-scratch is Other, kserve is Apache-2.0. - Tags unique to LLMs-from-scratch: ai, attention mechanism, deep-learning, finetuning. - - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework. ## When NOT to use kserve - 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 LLMs-from-scratch - - If you are looking for a rapid deployment of an LLM without understanding its intricate structure - this tool requires extensive manual and conceptual work. - - You prefer frameworks with automatic model generation or other high-level abstractions that simplify the process. This repository emphasizes manual creation, which is more time-consuming but offers丰 - a deeper learning experience. ## Common questions ### What is the difference between kserve and LLMs-from-scratch? kserve: Standardized Distributed Generative and Predictive AI Inference Platform for Scalable, Multi-Framework Deployment on Kubernetes. LLMs-from-scratch: Implement a ChatGPT-like LLM in PyTorch from scratch, step by step. See the comparison table for live GitHub stats and shared categories. ### When should I choose kserve over LLMs-from-scratch? Choose kserve over LLMs-from-scratch when kserve is primarily Go; LLMs-from-scratch is Jupyter Notebook; License: kserve is Apache-2.0, LLMs-from-scratch is Other; Tags unique to kserve: cncf, genai, hacktoberfest, istio; Also covers Inference & Serving. ### When should I choose LLMs-from-scratch over kserve? Choose LLMs-from-scratch over kserve when LLMs-from-scratch is primarily Jupyter Notebook; kserve is Go; License: LLMs-from-scratch is Other, kserve is Apache-2.0; Tags unique to LLMs-from-scratch: ai, attention mechanism, deep-learning, finetuning; - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework. ### When should I avoid kserve? 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 LLMs-from-scratch? - If you are looking for a rapid deployment of an LLM without understanding its intricate structure - this tool requires extensive manual and conceptual work. - You prefer frameworks with automatic model generation or other high-level abstractions that simplify the process. This repository emphasizes manual creation, which is more time-consuming but offers丰 a deeper learning experience. ### Is kserve or LLMs-from-scratch more popular on GitHub? LLMs-from-scratch has more GitHub stars (98,899 vs 5,674). Stars measure visibility, not whether either tool fits your constraints. ### Are kserve and LLMs-from-scratch open source? Yes - both are open-source projects on GitHub (kserve: Apache-2.0, LLMs-from-scratch: Other). ### Where can I find alternatives to kserve or LLMs-from-scratch? GraphCanon lists graph-backed alternatives at [kserve alternatives](/tools/kserve-kserve/alternatives) and [LLMs-from-scratch alternatives](/tools/rasbt-llms-from-scratch/alternatives) ([kserve markdown twin](/tools/kserve-kserve/alternatives.md), [LLMs-from-scratch markdown twin](/tools/rasbt-llms-from-scratch/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/kserve-kserve-vs-rasbt-llms-from-scratch.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, kserve or LLMs-from-scratch? kserve: Very active. LLMs-from-scratch: 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 kserve and LLMs-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [kserve trust report](/tools/kserve-kserve/trust); [LLMs-from-scratch trust report](/tools/rasbt-llms-from-scratch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=kserve-kserve`](/api/graphcanon/graph?tool=kserve-kserve) - 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/_