The Unbeatable Local AI Coding Workflow (Full 2026 Setup)
Skills:
AI Pair Programming90%
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Run Claude Code with local AI models using LM Studio and a powerful GPU, no cloud API keys needed. In this video, I walk through my complete local AI coding setup: running Qwen 3.5 on an RTX 5090, linking my Linux GPU machine to my MacBook using LM Studio Link, and connecting Claude Code to local models through LM Studio's Anthropic-compatible API endpoint. Then I build a full-stack Next.js dashboard from scratch using only local AI to prove it actually works and I share the real limitations most YouTubers won't tell you about.
Sources & Documentation
- Claude Code LLM Gateway Configuration: https://code.claude.com/docs/en/llm-gateway
- Use Your LM Studio Models in Claude Code (LM Studio Blog): https://lmstudio.ai/blog/claudecode
- LM Link โ Use Your Local Models Remotely (Docs): https://lmstudio.ai/docs/lmlink
- LM Link Product Page: https://lmstudio.ai/link
- LM Studio Developer Docs (Local Server & API): https://lmstudio.ai/docs/developer
What You'll Learn
- How to run local AI models on your GPU for coding (Qwen 3.5 35B on RTX 5090)
- Why GPU offloading vs system RAM makes a huge difference in local AI speed
- How to use LM Studio Link to share models between machines (Linux GPU โ MacBook)
- How to connect Claude Code to local models via LM Studio's Anthropic-compatible endpoint
- Why Claude Code's system prompt makes local models much slower than empty chat
- The context window pitfall: why the default 4K tokens hangs Claude Code and how to fix it
- Building a real full-stack Next.js app with Claude Code and local AI models
- How to use sub-agents in Claude Code to maximize a limited local context window
- Running Claude Code in bypass-all-permissions mode safely with dev containers
- Honest comparison: local AI coding vs cloud models โ bugs, speed, and trade-offs
Timestamps
0:19 My Local AI Linux Machine
1:43 Expose
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