---
title: "text-embeddings-inference vs LLMs-from-scratch"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/huggingface-text-embeddings-inference-vs-rasbt-llms-from-scratch"
tools: ["huggingface-text-embeddings-inference", "rasbt-llms-from-scratch"]
---

# text-embeddings-inference vs LLMs-from-scratch

*GraphCanon updated Jul 11, 2026*

## Verdict

Pick text-embeddings-inference when text-embeddings-inference is primarily Rust; LLMs-from-scratch is Jupyter Notebook; pick LLMs-from-scratch when lLMs-from-scratch is primarily Jupyter Notebook; text-embeddings-inference is Rust.

[text-embeddings-inference](https://huggingface.co/docs/text-embeddings-inference/quick_tour) reports 4.9k GitHub stars, 411 forks, and 197 open issues, last pushed Jul 9, 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 [text-embeddings-inference's repository](https://github.com/huggingface/text-embeddings-inference) and [LLMs-from-scratch's repository](https://github.com/rasbt/LLMs-from-scratch).

| | [text-embeddings-inference](/tools/huggingface-text-embeddings-inference.md) | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) |
| --- | --- | --- |
| Tagline | A blazing fast inference solution for text embeddings models | Implement a ChatGPT-like LLM in PyTorch from scratch, step by step |
| Stars | 4,924 | 98,899 |
| Forks | 411 | 15,183 |
| Open issues | 197 | 4 |
| Language | Rust | 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, Vector Databases | LLM Frameworks, Model Training |

## Trust and health

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

| | [text-embeddings-inference](/tools/huggingface-text-embeddings-inference.md) | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) |
| --- | --- | --- |
| Maintenance | Very active (96%) | Steady (60%) |
| Days since push | 2d | 38d |
| Open issues (now) | 197 | 4 |
| Owner type | Organization | User |
| Full report | [trust report](/tools/huggingface-text-embeddings-inference/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 text-embeddings-inference if…

- text-embeddings-inference is primarily Rust; LLMs-from-scratch is Jupyter Notebook.
- License: text-embeddings-inference is Apache-2.0, LLMs-from-scratch is Other.
- Tags unique to text-embeddings-inference: embeddings, huggingface, llm, ml.
- Also covers Vector Databases.
- text-embeddings-inference ships Docker support for self-hosted deployment.

### Choose LLMs-from-scratch if…

- LLMs-from-scratch is primarily Jupyter Notebook; text-embeddings-inference is Rust.
- License: LLMs-from-scratch is Other, text-embeddings-inference is Apache-2.0.
- Tags unique to LLMs-from-scratch: artificial-intelligence, 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 text-embeddings-inference

- 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.
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.

## 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 text-embeddings-inference and LLMs-from-scratch?

text-embeddings-inference: A blazing fast inference solution for text embeddings models. 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 text-embeddings-inference over LLMs-from-scratch?

Choose text-embeddings-inference over LLMs-from-scratch when text-embeddings-inference is primarily Rust; LLMs-from-scratch is Jupyter Notebook; License: text-embeddings-inference is Apache-2.0, LLMs-from-scratch is Other; Tags unique to text-embeddings-inference: embeddings, huggingface, llm, ml; Also covers Vector Databases; text-embeddings-inference ships Docker support for self-hosted deployment.

### When should I choose LLMs-from-scratch over text-embeddings-inference?

Choose LLMs-from-scratch over text-embeddings-inference when LLMs-from-scratch is primarily Jupyter Notebook; text-embeddings-inference is Rust; License: LLMs-from-scratch is Other, text-embeddings-inference is Apache-2.0; Tags unique to LLMs-from-scratch: artificial-intelligence, 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 text-embeddings-inference?

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. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.

### 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 text-embeddings-inference or LLMs-from-scratch more popular on GitHub?

LLMs-from-scratch has more GitHub stars (98,899 vs 4,924). Stars measure visibility, not whether either tool fits your constraints.

### Are text-embeddings-inference and LLMs-from-scratch open source?

Yes - both are open-source projects on GitHub (text-embeddings-inference: Apache-2.0, LLMs-from-scratch: Other).

### Where can I find alternatives to text-embeddings-inference or LLMs-from-scratch?

GraphCanon lists graph-backed alternatives at [text-embeddings-inference alternatives](/tools/huggingface-text-embeddings-inference/alternatives) and [LLMs-from-scratch alternatives](/tools/rasbt-llms-from-scratch/alternatives) ([text-embeddings-inference markdown twin](/tools/huggingface-text-embeddings-inference/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/huggingface-text-embeddings-inference-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, text-embeddings-inference or LLMs-from-scratch?

text-embeddings-inference: 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 text-embeddings-inference and LLMs-from-scratch?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [text-embeddings-inference trust report](/tools/huggingface-text-embeddings-inference/trust); [LLMs-from-scratch trust report](/tools/rasbt-llms-from-scratch/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=huggingface-text-embeddings-inference`](/api/graphcanon/graph?tool=huggingface-text-embeddings-inference)
- 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/_