{"id":991792,"date":"2026-09-12T16:26:35","date_gmt":"2026-09-12T09:26:35","guid":{"rendered":"https:\/\/5id.vn\/deepseek-from-scratch-v3-2-and-r1-llms-in-python-or-rust\/"},"modified":"2026-09-12T16:26:35","modified_gmt":"2026-09-12T09:26:35","slug":"deepseek-from-scratch-v3-2-and-r1-llms-in-python-or-rust","status":"publish","type":"post","link":"https:\/\/5id.vn\/vi\/deepseek-from-scratch-v3-2-and-r1-llms-in-python-or-rust\/","title":{"rendered":"DeepSeek from scratch: V3.2 and R1 LLMs in Python or Rust"},"content":{"rendered":"<div class=\"vgblk-rw-wrapper limit-wrapper\">\n<div class=\"seo-article-body\" style=\"width:100%;max-width:830px;margin:0 auto;line-height:1.7;box-sizing:border-box\">\n<p>DeepSeek from scratch is an open-source repository that walks developers through the complete implementation of modern large language models. It targets two architectures &#8211; DeepSeek-V3.2 and DeepSeek-R1 &#8211; and lets you build them from the ground up using either Python or Rust.<\/p>\n<h2>DeepSeek from scratch Overview<\/h2>\n<p>The project lives up to its name by providing step-by-step instructions for constructing the DeepSeek models without relying on pre-compiled binaries. The repository is especially valuable for engineers who want to move beyond simply running a model to actually understanding its inner workings.<\/p>\n<h2>Language Options: Python and Rust<\/h2>\n<p>Both Python and Rust are supported, giving you the flexibility to choose a language that matches your performance needs and familiarity. Python offers rapid prototyping and a rich ecosystem of machine-learning libraries, while Rust delivers low-level control and high execution speed, which can be crucial for large-scale inference.<\/p>\n<h2>Key Innovations for Performance<\/h2>\n<p>The authors highlight several &quot;key innovations&quot; that make the DeepSeek implementations fast, powerful, and efficient. These include optimized attention mechanisms, memory-friendly tensor handling, and custom kernels that reduce latency during training and inference.<\/p>\n<h2>Learning Benefits<\/h2>\n<p>This repo is designed as a hands-on learning platform. By assembling each component of a state-of-the-art LLM, you gain a systematic grasp of model architecture, data pipelines, and training dynamics. It is ideal for anyone seeking a structured path from merely using a model to truly understanding its design.<\/p>\n<h2>Conclusion<\/h2>\n<p>DeepSeek from scratch offers a rare opportunity to dive deep into LLM construction with clear guidance, language choice flexibility, and performance-focused innovations. Whether you are a researcher, a developer, or an enthusiast, the repository provides the tools needed to master modern language-model engineering.<\/p>\n<\/div>\n<\/div>\n<p><!-- .vgblk-rw-wrapper --><\/p>","protected":false},"excerpt":{"rendered":"<p>Discover DeepSeek from scratch, a little-known repo that guides you to build DeepSeek V3.2 and R1 LLMs from the ground up using Python or Rust.<\/p>","protected":false},"author":26,"featured_media":991791,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_yoast_wpseo_focuskw":"DeepSeek from scratch","_yoast_wpseo_title":"","_yoast_wpseo_metadesc":"Discover DeepSeek from scratch, a little-known repo that guides you to build DeepSeek V3.2 and R1 LLMs from the ground up using Python or Rust.","_yoast_wpseo_linkdex":"90","_yoast_wpseo_content_score":"90","rank_math_focus_keyword":"","rank_math_description":"","rank_math_title":"","source_url":"","source_name":"Staff-AI","footnotes":""},"categories":[17,19,11],"tags":[],"class_list":["post-991792","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-development","category-it","category-news"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.5 (Yoast SEO v27.5) - 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