Hit your Claude Code quota limit mid-project? You don't have to stop coding. Here's how to connect Claude Code to local open-source models and keep building.

This guide covers everything you need to switch from Anthropic's servers to running models locally—perfect for when you're deep in a coding session and don't want to wait for your quota to reset.

Why Use Local Models with Claude Code?

Claude Code's quota system can catch you at the worst times. If you're on a cheaper Anthropic plan, daily or weekly limits can interrupt your flow right when you're making progress.

Local models solve this by:

No quota limits — Run as long as you want
Privacy — Code stays on your machine
Cost savings — No API charges
Offline capability — Work without internet

The tradeoff? Local models are slower and less capable than Claude. But they're good enough to keep momentum when you're stuck.

Check Your Current Quota

Before switching, check how much quota you have left:

/usage

This shows your remaining tokens, burn rate, and when limits reset.

Best Local Models for Coding (2026)

The open-source model landscape changes fast. As of February 2026, these are the top picks:

Model	Best For	VRAM Required
GLM-4.7-Flash	General coding, fast responses	16GB+
Qwen3-Coder-Next	Code generation, debugging	24GB+
DeepSeek-Coder-V3	Complex reasoning	32GB+

If you're low on GPU memory, use quantized versions (4-bit or 8-bit) for smaller footprint at some quality cost.

Method 1: LM Studio (Recommended)

LM Studio is the easiest way to run local models. Version 0.4.1+ has native Claude Code support.

Step 1: Install LM Studio

Download from lmstudio.ai/download and install for your platform.

Step 2: Download a Model

Open LM Studio
Click the search button
Search for "GLM-4.7-Flash" or "Qwen3-Coder"
Download a version that fits your VRAM
LM Studio recommends 25K+ context for coding tasks

Step 3: Start the Server

Open a terminal and run:

lms server start --port 1234

Step 4: Configure Claude Code

Set environment variables to point Claude Code at your local server:

export ANTHROPIC_BASE_URL=http://localhost:1234
export ANTHROPIC_AUTH_TOKEN=lmstudio

Step 5: Start Claude Code

claude --model openai/gpt-oss-20b

To verify which model you're using, type:

/model

Method 2: Llama.cpp (Advanced)

LM Studio is built on llama.cpp. If you prefer direct control:

Clone and build llama.cpp
Download a GGUF model file
Start the server with your model
Point Claude Code at it using the same environment variables

See the Unsloth guide for detailed instructions.

Switching Back to Claude

When your quota resets, switch back to full Claude power:

/model claude-opus-4-6

Or unset the environment variables and restart Claude Code.

Performance Expectations

Be realistic about local models:

Speed — 2-10x slower than Claude API
Quality — Good for straightforward tasks, struggles with complex architecture
Context — Often limited to 32K tokens vs Claude's 200K+

Local models are a backup, not a replacement. Use them to maintain momentum, then switch back for heavy lifting.

Alternative: Serenities AI

If quota management is a constant headache, consider Serenities AI. We let you connect your existing AI subscriptions (ChatGPT Plus, Claude Pro) instead of paying per-token API rates—typically 10-25x cheaper than direct API access.

No quota surprises. No local setup required. Just AI automation that works.

Conclusion

Local models aren't a replacement for Claude's intelligence, but they're a lifesaver when you're mid-project and hit a quota wall. Set up LM Studio once, and you'll always have a fallback ready.

Keep coding. 🚀

Claude Code: How to Use Local Models When Your Quota Runs Out