Why Code with a Local LLM
There are three compelling reasons developers are switching from cloud-based coding AI to local models running on Apple Silicon.
- Privacy: Your code never leaves your machine. No telemetry, no training on your proprietary logic, no risk of sensitive IP leaking through an API call. For enterprise developers bound by NDAs or working on pre-release products, this is non-negotiable.
- Speed and availability: Local inference eliminates network round-trips. On an M-series Mac, code completions arrive in under 200 milliseconds with no dependence on server availability, rate limits, or internet connectivity. You can code on a plane at full speed.
- Zero recurring cost: Cloud coding assistants charge $10-40 per month. A local model is a one-time download. According to LLMCheck data, the average developer recoups the equivalent subscription cost within three months of local-only usage.
Top Coding Models Ranked
According to LLMCheck benchmarks run on Apple Silicon hardware, here are the best local models for coding tasks in March 2026, ranked by SWE-Bench Verified score:
| Model | SWE-Bench | HumanEval | Min RAM | tok/s | License |
|---|---|---|---|---|---|
| Qwen3-Coder-Next | 70.6% | 94.2% | 64 GB | ~18 | Apache 2.0 |
| DeepSeek-Coder V2 | 62.1% | 90.8% | 32 GB | ~30 | DeepSeek |
| Qwen 3.5 35B MoE | 58.4% | 89.5% | 32 GB | ~45 | Apache 2.0 |
| Qwen 3.5 9B | 47.2% | 84.1% | 16 GB | ~100 | Apache 2.0 |
| Phi-4 Mini | 38.9% | 79.6% | 8 GB | ~135 | MIT |
| Llama 3.1 8B | 34.5% | 76.3% | 8 GB | ~120 | Llama 3.1 |
Key takeaway: Qwen3-Coder-Next at 70.6% SWE-Bench is the first local model to genuinely compete with cloud-tier coding assistants. If you have 64 GB RAM, it should be your default coding model.
Quick Setup with Ollama
Getting a coding LLM running on your Mac takes less than two minutes. Ollama handles model downloading, quantization, and Metal GPU acceleration automatically.
Install Ollama and pull your first coding model with a single terminal command:
curl -fsSL https://ollama.com/install.sh | sh && ollama pull qwen3.5:9b
Once downloaded, run ollama run qwen3.5:9b and start prompting. The model loads entirely into Unified Memory and uses Apple's Metal framework for GPU-accelerated inference. No Python environment, no Docker containers, no CUDA drivers needed.
For the top-tier Qwen3-Coder-Next on a 64 GB Mac, use: ollama pull qwen3-coder-next. The download is approximately 38 GB and takes 10-20 minutes on a typical broadband connection.
IDE Integration Tips
A coding model is most useful when it lives inside your editor. Here are the two best options for connecting a local LLM to your development workflow:
- Continue (VS Code / JetBrains): Open-source extension that connects to your local Ollama instance. Supports inline completions, chat, and multi-file context. Point it to
http://localhost:11434and select your model. According to LLMCheck testing, Continue with Qwen 3.5 9B provides the best balance of speed and quality for real-time tab completions. - Aider (Terminal): A terminal-based coding assistant that works with local models via Ollama. Excellent for refactoring tasks and multi-file edits. Use
aider --model ollama/qwen3.5:35bfor complex architectural changes.
Both tools keep everything local. No API keys, no cloud accounts, no data leaving your machine.
When to Use Local vs. Cloud for Coding
Local coding AI is not always the right choice. Here is an honest comparison to help you decide:
- Use local when: You work with proprietary code, need offline access, want zero recurring costs, or require sub-200ms latency for inline completions. Local models excel at single-file tasks: function generation, code review, documentation, and test writing.
- Use cloud when: You need massive context windows (200k+ tokens) for understanding entire codebases at once, or you rely on features like real-time web search integration within your coding assistant. Cloud models also have an edge for niche languages with limited training data.
- Use both: Many developers run a local model for day-to-day completions and keep a cloud subscription for complex multi-file refactoring sessions. This hybrid approach minimizes cost while maximizing capability.