--- title: "sarathi-serve vs LLMs-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/microsoft-sarathi-serve-vs-rasbt-llms-from-scratch" tools: ["microsoft-sarathi-serve", "rasbt-llms-from-scratch"] --- # sarathi-serve vs LLMs-from-scratch *GraphCanon updated Jul 11, 2026* ## Verdict Pick sarathi-serve when sarathi-serve is primarily Python; LLMs-from-scratch is Jupyter Notebook; pick LLMs-from-scratch when lLMs-from-scratch is primarily Jupyter Notebook; sarathi-serve is Python. [sarathi-serve](https://github.com/microsoft/sarathi-serve) reports 509 GitHub stars, 64 forks, and 16 open issues, last pushed Jan 8, 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 [sarathi-serve's repository](https://github.com/microsoft/sarathi-serve) and [LLMs-from-scratch's repository](https://github.com/rasbt/LLMs-from-scratch). | | [sarathi-serve](/tools/microsoft-sarathi-serve.md) | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) | | --- | --- | --- | | Tagline | A low-latency & high-throughput serving engine for LLMs | Implement a ChatGPT-like LLM in PyTorch from scratch, step by step | | Stars | 509 | 98,899 | | Forks | 64 | 15,183 | | Open issues | 16 | 4 | | Language | Python | 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._ | | [sarathi-serve](/tools/microsoft-sarathi-serve.md) | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Steady (60%) | | Days since push | 184d | 38d | | Open issues (now) | 16 | 4 | | Owner type | Organization | User | | Full report | [trust report](/tools/microsoft-sarathi-serve/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 sarathi-serve if… - sarathi-serve is primarily Python; LLMs-from-scratch is Jupyter Notebook. - License: sarathi-serve is Apache-2.0, LLMs-from-scratch is Other. - Tags unique to sarathi-serve: llama, llm-inference, python, pytorch. - Also covers Inference & Serving. ### Choose LLMs-from-scratch if… - LLMs-from-scratch is primarily Jupyter Notebook; sarathi-serve is Python. - License: LLMs-from-scratch is Other, sarathi-serve is Apache-2.0. - Tags unique to LLMs-from-scratch: ai, artificial-intelligence, attention mechanism, deep-learning. - - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework. ## When NOT to use sarathi-serve - Last GitHub push was 185 days ago (slowing maintenance, Jan 8, 2026). Validate activity before betting a new project on sarathi-serve. - 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 sarathi-serve and LLMs-from-scratch? sarathi-serve: A low-latency & high-throughput serving engine for LLMs. 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 sarathi-serve over LLMs-from-scratch? Choose sarathi-serve over LLMs-from-scratch when sarathi-serve is primarily Python; LLMs-from-scratch is Jupyter Notebook; License: sarathi-serve is Apache-2.0, LLMs-from-scratch is Other; Tags unique to sarathi-serve: llama, llm-inference, python, pytorch; Also covers Inference & Serving. ### When should I choose LLMs-from-scratch over sarathi-serve? Choose LLMs-from-scratch over sarathi-serve when LLMs-from-scratch is primarily Jupyter Notebook; sarathi-serve is Python; License: LLMs-from-scratch is Other, sarathi-serve is Apache-2.0; Tags unique to LLMs-from-scratch: ai, artificial-intelligence, attention mechanism, deep-learning; - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework. ### When should I avoid sarathi-serve? Last GitHub push was 185 days ago (slowing maintenance, Jan 8, 2026). Validate activity before betting a new project on sarathi-serve. 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 sarathi-serve or LLMs-from-scratch more popular on GitHub? LLMs-from-scratch has more GitHub stars (98,899 vs 509). Stars measure visibility, not whether either tool fits your constraints. ### Are sarathi-serve and LLMs-from-scratch open source? Yes - both are open-source projects on GitHub (sarathi-serve: Apache-2.0, LLMs-from-scratch: Other). ### Where can I find alternatives to sarathi-serve or LLMs-from-scratch? GraphCanon lists graph-backed alternatives at [sarathi-serve alternatives](/tools/microsoft-sarathi-serve/alternatives) and [LLMs-from-scratch alternatives](/tools/rasbt-llms-from-scratch/alternatives) ([sarathi-serve markdown twin](/tools/microsoft-sarathi-serve/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/microsoft-sarathi-serve-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, sarathi-serve or LLMs-from-scratch? sarathi-serve: Slowing. 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 sarathi-serve and LLMs-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [sarathi-serve trust report](/tools/microsoft-sarathi-serve/trust); [LLMs-from-scratch trust report](/tools/rasbt-llms-from-scratch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=microsoft-sarathi-serve`](/api/graphcanon/graph?tool=microsoft-sarathi-serve) - 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/_