---
title: "long-context-attention vs LLMs-from-scratch"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/feifeibear-long-context-attention-vs-rasbt-llms-from-scratch"
tools: ["feifeibear-long-context-attention", "rasbt-llms-from-scratch"]
---

# long-context-attention vs LLMs-from-scratch

*GraphCanon updated Jul 11, 2026*

## Verdict

Pick long-context-attention when long-context-attention is primarily Python; LLMs-from-scratch is Jupyter Notebook; pick LLMs-from-scratch when lLMs-from-scratch is primarily Jupyter Notebook; long-context-attention is Python.

[long-context-attention](https://github.com/feifeibear/long-context-attention) reports 678 GitHub stars, 80 forks, and 12 open issues, last pushed May 21, 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 [long-context-attention's repository](https://github.com/feifeibear/long-context-attention) and [LLMs-from-scratch's repository](https://github.com/rasbt/LLMs-from-scratch).

| | [long-context-attention](/tools/feifeibear-long-context-attention.md) | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) |
| --- | --- | --- |
| Tagline | USP: Unified (a.k.a. Hybrid, 2D) Sequence Parallel Attention for Long Context Transformers Model Training and Inference | Implement a ChatGPT-like LLM in PyTorch from scratch, step by step |
| Stars | 678 | 98,899 |
| Forks | 80 | 15,183 |
| Open issues | 12 | 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 | LLM Frameworks, Model Training, Inference & Serving | LLM Frameworks, Model Training |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [long-context-attention](/tools/feifeibear-long-context-attention.md) | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) |
| --- | --- | --- |
| Days since push | 51d | 38d |
| Open issues (now) | 12 | 4 |
| Full report | [trust report](/tools/feifeibear-long-context-attention/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 long-context-attention if…

- long-context-attention is primarily Python; LLMs-from-scratch is Jupyter Notebook.
- License: long-context-attention is Apache-2.0, LLMs-from-scratch is Other.
- Tags unique to long-context-attention: ring-attention, python, llm-inference, pytorch.
- Also covers Inference & Serving.

### Choose LLMs-from-scratch if…

- LLMs-from-scratch is primarily Jupyter Notebook; long-context-attention is Python.
- License: LLMs-from-scratch is Other, long-context-attention is Apache-2.0.
- Tags unique to LLMs-from-scratch: deep-learning, ai, artificial-intelligence, attention-mechanism.
- - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework.

## When NOT to use long-context-attention

- 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.
- Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.

## 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 long-context-attention and LLMs-from-scratch?

long-context-attention: USP: Unified (a.k.a. Hybrid, 2D) Sequence Parallel Attention for Long Context Transformers Model Training and Inference. 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 long-context-attention over LLMs-from-scratch?

Choose long-context-attention over LLMs-from-scratch when long-context-attention is primarily Python; LLMs-from-scratch is Jupyter Notebook; License: long-context-attention is Apache-2.0, LLMs-from-scratch is Other; Tags unique to long-context-attention: ring-attention, python, llm-inference, pytorch; Also covers Inference & Serving.

### When should I choose LLMs-from-scratch over long-context-attention?

Choose LLMs-from-scratch over long-context-attention when LLMs-from-scratch is primarily Jupyter Notebook; long-context-attention is Python; License: LLMs-from-scratch is Other, long-context-attention is Apache-2.0; Tags unique to LLMs-from-scratch: deep-learning, ai, artificial-intelligence, attention-mechanism; - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework.

### When should I avoid long-context-attention?

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. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.

### 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 long-context-attention or LLMs-from-scratch more popular on GitHub?

LLMs-from-scratch has more GitHub stars (98,899 vs 678). Stars measure visibility, not whether either tool fits your constraints.

### Are long-context-attention and LLMs-from-scratch open source?

Yes - both are open-source projects on GitHub (long-context-attention: Apache-2.0, LLMs-from-scratch: Other).

### Where can I find alternatives to long-context-attention or LLMs-from-scratch?

GraphCanon lists graph-backed alternatives at [long-context-attention alternatives](/tools/feifeibear-long-context-attention/alternatives) and [LLMs-from-scratch alternatives](/tools/rasbt-llms-from-scratch/alternatives) ([long-context-attention markdown twin](/tools/feifeibear-long-context-attention/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/feifeibear-long-context-attention-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, long-context-attention or LLMs-from-scratch?

long-context-attention: Steady. 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 long-context-attention and LLMs-from-scratch?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [long-context-attention trust report](/tools/feifeibear-long-context-attention/trust); [LLMs-from-scratch trust report](/tools/rasbt-llms-from-scratch/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=feifeibear-long-context-attention`](/api/graphcanon/graph?tool=feifeibear-long-context-attention)
- 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/_