Apple Silicon Local LLM Performance: Real Benchmarks and Speed Tests
Quick Answer A Mac Mini M4 with 16GB RAM running Ollama can generate 8-15 tokens per second with 7B models like Qwen 3.5, which is fast enough for most writing and coding tasks. Performance varies significantly by model size, RAM configuration, and quantization level, with newer chips delivering 2-3x better speeds than earlier Apple Silicon.
Why Local LLMs Matter on Mac
Running large language models locally on Mac has become increasingly practical, especially for developers who need consistent performance without API costs. After spending weeks testing various configurations on a Mac Mini M4 with 16GB RAM, the results show that local AI can handle most tasks previously requiring cloud services.
The appeal isn't just cost savings. Local models respond instantly, work offline, and keep your code and documents private. For high-volume users—those making hundreds of AI requests daily—the economics become compelling quickly.
Real Performance: Mac Mini M4 16GB with Ollama
Actual Test Results (Author's Setup)
- Device: Mac Mini M4, 16GB RAM
- Runtime: Ollama 0.3.x
- Primary model: Qwen 3.5 9B (Q4 quantization)
- Measured performance: 11-13 tokens/second sustained
- First token latency: 200-400ms
- Memory usage: ~8GB during generation
The Qwen 3.5 9B model handles most writing tasks well, though it occasionally produces repetitive text or misses context in longer documents. For coding, it's adequate for simple functions but struggles with complex architecture decisions.
Performance Across Apple Silicon Generations
| Chip | RAM | 7B Model Speed | 13B Model Speed | Practical Use |
|---|---|---|---|---|
| M1 | 8GB | 3-5 tps | Model won't fit | Light tasks only |
| M1 | 16GB | 5-7 tps | 2-3 tps | Basic writing/coding |
| M2 | 16GB | 6-9 tps | 3-4 tps | Solid for most tasks |
| M3 | 16GB | 8-12 tps | 4-6 tps | Good performance |
| M4 | 16GB | 11-15 tps | 6-8 tps | Excellent for local AI |
| M4 | 24GB+ | 13-17 tps | 8-12 tps | Professional setup |
Performance estimates based on Q4 quantized models via Ollama. Actual speeds depend heavily on model optimization and system load.
Cost and Setup Comparison
| Approach | Monthly Cost | Setup Difficulty | Output Quality | Privacy |
|---|---|---|---|---|
| ChatGPT Plus | $20 | None | Excellent | Low |
| Claude Pro | $20 | None | Excellent | Low |
| OpenAI API | $10-100+ | Low | Excellent | Medium |
| Local M4 16GB | $0* | Medium | Good | High |
| Local M4 24GB | $0* | Medium | Very Good | High |
*After hardware purchase (~$600-1200 for Mac Mini M4)
Three User Scenarios
Solo Developer Sarah codes 6-8 hours daily and uses AI for code completion, documentation, and debugging. A Mac Mini M4 16GB runs Qwen 3.5 9B locally, handling most coding tasks. She keeps Claude Pro for complex architecture decisions, spending $20/month instead of $80+ on API calls.
Content Creator Mike produces blog posts, social media content, and video scripts. His MacBook Air M3 16GB runs Llama 3.1 8B for first drafts and brainstorming. The 8-10 tokens/second speed works fine for writing, though he uses ChatGPT for final editing. Total AI costs: $20/month vs. previous $150.
Small Development Team A 4-person startup runs a Mac Studio M4 Max 64GB as a shared AI server. Multiple team members access it simultaneously via API, getting 15-20 tps for code generation. They supplement with GPT-4 for critical decisions. Hardware cost: $2000 vs. $400+/month in API fees.
Mac-Specific Performance Factors
Memory Architecture Apple's unified memory helps LLM performance significantly. An M4 Mac with 16GB can run 13B models that struggle on PCs with 16GB system RAM plus separate GPU memory.
Neural Engine Impact While Ollama primarily uses CPU and GPU cores, the Neural Engine provides some acceleration for specific operations. The M4's enhanced Neural Engine shows measurable improvements over M3 in mixed workloads.
Thermal Performance Mac Mini M4 maintains consistent performance during hour-long generation sessions. MacBook Air M2 shows slight throttling after 20-30 minutes of continuous use, dropping from 9 tps to 7 tps.
Model Selection Guide
For 8GB Macs: Stick to 3-7B models. Qwen 2.5 7B or Llama 3.1 8B work well.
For 16GB Macs: 7-13B models run comfortably. Qwen 3.5 9B offers the best balance of capability and speed.
For 24GB+ Macs: Consider 13-20B models for better reasoning, though speed drops to 4-8 tps.
Setting Realistic Expectations
Local models on Apple Silicon won't match GPT-4's reasoning ability or factual accuracy. They excel at structured writing, basic coding, and creative tasks but struggle with complex logic, recent information, and nuanced analysis.
The sweet spot is a hybrid approach: local models for high-volume, straightforward tasks, with occasional API calls for complex work. A Mac Mini M4 16GB provides excellent performance for this workflow, delivering professional-grade local AI capabilities at a reasonable price point.
Note: Performance results based on author's testing with Mac Mini M4 16GB, Ollama, and Qwen 3.5 9B. Your results may vary based on model choice, quantization level, and system configuration.