# Deepseek v3 101

> An accessible technical guide explaining the architecture of DeepSeek-V3

Deepseek v3 101 is a Notion-hosted technical guide breaking down the architecture of the DeepSeek-V3 base model. It explains key design choices like Multi-Head Latent Attention and compares its benchmark performance against models like Llama 3.1 405B, Qwen, and Mistral. It provides a readable overview of how open models compete with frontier closed models.

Website: https://lunar-joke-35b.notion.site/Deepseek-v3-101-169ba4b6a3fa8090a7aacaee1a1cefaa

## What it is for

- Understand the underlying architecture of DeepSeek-V3
- Study the implementation and advantages of Multi-Head Latent Attention
- Compare DeepSeek-V3 benchmark performance against other open and closed LLMs

## Features

- Architectural breakdown of DeepSeek-V3
- Detailed explanation of Multi-Head Latent Attention (MLA)
- Benchmark comparisons against Llama 3.1 405B, Qwen, Mistral, and closed models
- Notion-based readable reference notes

## Pricing

Free to read

## Details

- Type: Article or course
- Category: [Learning](https://xiry.app/topics/learning.md)
- Open source: no
- Platforms: Web
- For: Machine learning engineers, researchers, and AI enthusiasts
- Tags: deepseek, llm, machine learning, architecture, ai research

## Why it was saved

- [@cneuralnetwork, 26 January 2025](https://x.com/cneuralnetwork/status/1883418047802912992): Detailed technical explainer of Deepseek V3's architecture, written accessibly.

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Updated 10 October 2026. Page: https://xiry.app/resources/deepseek-v3-101

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