AI Tools: Unlimited Claude Code For Free (Runs On Your Computer)

Watch on YouTube: https://www.youtube.com/watch?v=K8lVHiFnN2A

Channel: Sharbel A.

Running Claude Code Without the Subscription: Three Free Paths

Claude Code has become a popular AI-powered coding assistant, but its official pricing model ties usage to Anthropic’s hosted models. A recent walkthrough by Sharbel A. outlines three practical ways to use the Claude Code tooling for free by swapping the underlying model for local or free-tier alternatives. The core insight: Claude Code is an agent harness — a tool that orchestrates tasks — while the model is the “brain” doing the reasoning. You pay Anthropic for the brain, not the harness. By pointing the harness at free models, you keep the workflow without the recurring cost.

What the Video Covers

• Local execution via Ollama — run open-weight models on your own hardware, trading RAM and speed for privacy and zero API fees.
• Free LLM API aggregation — a Docker-based gateway that pools free tiers from Google AI Studio, Grok, and Mistral behind a single local endpoint.
• Open Code — a separate open-source coding agent (opencode.ai) that supports free models out of the box with minimal configuration.

Each approach has distinct hardware requirements, privacy implications, and setup complexity. The video emphasizes matching the method to your machine’s capabilities and your tolerance for latency.

Step-by-Step Walkthrough

Option 1: Ollama + Local Models

1. Assess your RAM. The model you can run depends on available memory:
   • 8–16 GB system RAM → Gemma 4 12b
   • 18–32 GB system RAM → Qwen 3 Coder
   • 64 GB+ system RAM → GPT OSS 120
2. Mac unified memory adjustment. On Apple Silicon, the GPU shares system RAM. Pick a model sized for roughly half your total RAM (e.g., an 18 GB Mac → 9 GB model like Gemma 4 12b).
3. Install Ollama. Download from ollama.com for your OS.
4. Pull the model. Example: ollama run gemma2:12b (adjust tag per the creator’s model-selection guide).
5. Connect to Claude Code. Open the Ollama app → Apps → “Claude Code in other apps” → copy the provided ollama launch claude command into your terminal.

Option 2: Free LLM API (Docker Gateway)

1. Install Docker. Get it from docker.com.
2. Deploy the Free LLM API container. The GitHub repo (linked in the video description) provides the compose file.
3. Add free-tier API keys. Via the dashboard (port 3000 or 3001), enter keys for:
   • Google AI Studio (free tier)
   • Grok free tier
   • Mistral free tier
4. Point Claude Code to localhost:3000 (or 3001). The gateway routes requests to whichever provider has quota.

Option 3: Open Code

1. Visit opencode.ai and follow the installation instructions for your platform.
2. Configure a free model (local via Ollama or remote free tier) in the settings.
3. Start coding — no separate Claude Code harness needed.

Tools, Models, and Numbers at a Glance

• Ollama — local model runner (ollama.com)
• Models referenced: Gemma 4 12b, Qwen 3 Coder, GPT OSS 120
• RAM thresholds: 8–16 GB, 18–32 GB, 64 GB+
• Mac rule of thumb: model size ≈ 50 % of total unified memory
• Free LLM API — Docker-based aggregator (GitHub repo)
• Free-tier providers: Google AI Studio, Grok, Mistral
• Dashboard ports: 3000 / 3001
• Open Code — alternative open-source agent (opencode.ai)

Caveats and Hype Check

Running a model locally is the private way to do this, but it’s not the only way to get Claude Code for free.

The “unlimited” claim holds only if you ignore the hidden costs:

• Hardware floor. Local models need 8 GB RAM minimum; usable performance starts around 16 GB. Many laptops won’t qualify.
• Speed trade-off. On modest hardware, token generation can be seconds per step — acceptable for experimentation, painful for production workflows.
• Privacy vs. convenience. Free LLM API sends code to third-party free tiers; local keeps everything on-device but consumes your compute and electricity.
• Tool divergence. Open Code is a different agent with its own UX; it’s not a drop-in Claude Code clone.

The creator is transparent about these limits and offers a free model-selection guide (newsletter signup) rather than promising magic results.

Concrete Next Actions

• Audit your machine. Check total RAM (and unified memory on Mac) against the thresholds above.
• Pick a path.
  • If you have ≥16 GB RAM and value privacy → Ollama + local model.
  • If you have limited RAM but free API quotas → Free LLM API via Docker.
  • If you want zero-config and don’t mind a new tool → Open Code.
• Execute the first two steps of your chosen path today. Install Ollama or Docker or Open Code, then pull a model or add an API key.
• Benchmark. Run a realistic coding task (e.g., “refactor this React component”) and measure latency. Decide if the speed is acceptable for your daily loop.
• Iterate. Swap models or providers as your hardware or quota changes; the harness stays the same.