Run DeepSeek R1 Locally with LM Studio – 100% Free & Private!
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In this video, I’ll walk you through setting up DeepSeek R1 on your local machine using LM Studio—no cloud, no API keys, and complete privacy! DeepSeek R1 is an open-source reasoning model that rivals top-tier AI models, and now you can run it entirely offline.
What You'll Learn:
✅ How to install and set up LM Studio
✅ Downloading & running DeepSeek R1 locally
✅ Performance benchmarks and real-world tests
✅ Pros, cons, and practical use cases
✅ How to enhance privacy and efficiency
💻 Requirements:
A computer with a decent GPU (Mac M-series, Windows, or Linux supported)
LM Studio installed (free & open-source)
DeepSeek R1 model file (we’ll show you how to get it)
⚡ Why Run AI Locally?
100% Private: No data leaves your machine
Free Forever: No API keys, no subscriptions
Faster & Reliable: No rate limits, no downtime
🔗 Links & resources:
Skool: https://www.skool.com/ai-software-dev...
Newsletter: https://aidev9.substack.com
Discord server: / discord
GitHub repo: https://github.com/aidev9/tuts/tree/m...
PydanticAI: https://ai.pydantic.dev
LM Studio: https://lmstudio.ai
🎓 Who is this for?
Whether you’re an AI developer, data scientist, or enthusiast, this tutorial provides actionable insights into building intelligent, structured agent systems
PydanticAI Masterclass Series:
▶️ Part 1: • The Best Agent Framework Has Arrived ...
▶️ Part 2: • From Chaos to Clarity: LLM Tracing wi...
▶️ Part 3: • 100% Reliable LLM Outputs with Struct...
▶️ Part 4: • Dramatic Improvement! Design Better A...
▶️ Part 5: • Design Better AI Agents With Function...
▶️ Part 6: • Transform You Agents with Result Vali...
▶️ Part 7: Simplify Agent Workflows with Dependency Injection in PydanticAI
▶️ Part 8: Advanced Retry Strategies for High-Performance AI Agents in PydanticAI
▶️ Part 9: Better Context Retention with Agent Memory in PydanticAI
▶️ Part 10: Building Resilient Agents: Best Practices for Handling Model Errors in PydanticAI
▶️ Part 11: Better User Experience with Streaming Outputs in PydanticAI
▶️ Part 12: Achieving Precision and Efficiency with Advanced Model Settings
▶️ Part 13: Multi-Model Agents in PydanticAI: Unlocking Next-Gen AI Capabilities
▶️ Part 14: Mastering RAG in PydanticAI: Better AI Agents with Real-Time Data
▶️ Part 15: Masterclass Final Project: AI Resume Writing with Multiple Agents
Timecodes:
00:00 - Welcome to DeepSeek R1 + LM Studio
00:08 - What is LM Studio?
00:46 - Download LM Studio from website
01:36 - Install LM Studio with Homebrew
02:06 - Explore LM Studio
02:50 - Download DeepSeek R1
03:26 - Load the model
04:02 - Test question
05:04 - More questions
06:22 - Reasoning models - pros and cons
06:22 - Limitations of reasoning models
08:26 - Overcoming limitations with PydanticAI
09:14 - Summary
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