Introduction
The rise of local AI has transformed how professionals and enthusiasts interact with large language models. Running AI models locally offers significant advantages: complete data privacy, no recurring subscription costs, offline functionality, and freedom from rate limits. However, the performance of local AI systems varies dramatically depending on hardware choices.
Apple Silicon has emerged as a compelling platform for local AI deployment, leveraging unified memory architecture and efficient neural processing capabilities. But which Apple system delivers the best balance of performance, capability, and value for running local language models?
Motivation
Choosing the right hardware for local AI can be challenging. While cloud-based AI services like ChatGPT and Claude offer convenience, they come with privacy concerns, ongoing costs, and dependency on internet connectivity. Local AI eliminates these issues but requires careful hardware selection to ensure adequate performance.
This comprehensive benchmark comparison aims to answer critical questions:
- How does the Mac Studio compare to the more affordable Mac Mini M4?
- What performance trade-offs exist when scaling from tiny (1B) to medium (14B) models?
- Which configurations provide acceptable interactive performance?
- Where do Apple Silicon systems stand compared to dedicated GPU solutions?
All benchmarks were conducted using LocalScore AI, a standardized testing platform that measures generation speed, response latency, and prompt processing capabilities across different hardware and model configurations. LocalScore provides consistent, comparable metrics that help users make informed hardware decisions for local AI deployment.
Important Context: While Apple Silicon delivers impressive performance for integrated systems, it’s worth noting that dedicated GPU solutions like the NVIDIA RTX 4090 still significantly outperform these configurations in raw AI inference speed. However, Apple Silicon offers competitive performance within its thermal and power constraints, making it an excellent choice for users prioritizing system integration, energy efficiency, and silent operation over maximum throughput.
Key Takeaway
The Mac Studio dominates local AI performance across all model sizes, delivering 2-10x better speeds than the Mac Mini M4 depending on configuration.
Quick Recommendation: Choose Mac Studio for professional work or if you want to run 8B+ models. Choose Mac Mini M4 only if you’re budget-constrained and committed to tiny (1B) models exclusively.
Complete Performance Results
Both systems were tested with tiny (1B), small (8B), and medium (14B) models using Q4_K medium quantization on November 13, 2025.
| Metric | Mac Studio (1B) | Mac Mini M4 (1B) | Mac Studio (8B) | Mac Mini M4 (8B) | Mac Studio (14B) | Mac Mini M4 (14B) |
|---|---|---|---|---|---|---|
| Model | Llama 3.2 1B | Llama 3.2 1B | Llama 3.1 8B | Llama 3.1 8B | Qwen2.5 14B | Qwen2.5 14B |
| Generation Speed | 178 tokens/s | 77.1 tokens/s | 62.7 tokens/s | 17.7 tokens/s | 35.8 tokens/s | 9.6 tokens/s |
| Time to First Token | 203 ms | 1,180 ms | 1,060 ms | 6,850 ms | 2,040 ms | 13,300 ms |
| Prompt Processing | 5,719 tokens/s | 1,111 tokens/s | 1,119 tokens/s | 186 tokens/s | 583 tokens/s | 96 tokens/s |
| LocalScore Rating | 1,713 | 417 | 405 | 78 | 217 | 41 |
Performance Analysis by Model Size
Tiny Model (1B Parameters)
| Metric | Mac Studio | Mac Mini M4 | Performance Ratio |
|---|---|---|---|
| Generation Speed | 178 tokens/s | 77.1 tokens/s | 2.3x faster |
| Time to First Token | 203 ms | 1,180 ms | 5.8x faster |
| Prompt Processing | 5,719 tokens/s | 1,111 tokens/s | 5.1x faster |
| LocalScore Rating | 1,713 | 417 | 4.1x higher |
Mac Studio: Delivers exceptional performance with near-instantaneous 203ms response time and high throughput. Excellent for real-time coding assistance, content creation, and interactive workflows.
Mac Mini M4: Provides functional performance with noticeable 1.18-second latency. Adequate for occasional use and non-critical applications.
Small Model (8B Parameters)
| Metric | Mac Studio | Mac Mini M4 | Performance Ratio |
|---|---|---|---|
| Generation Speed | 62.7 tokens/s | 17.7 tokens/s | 3.5x faster |
| Time to First Token | 1,060 ms | 6,850 ms | 6.5x faster |
| Prompt Processing | 1,119 tokens/s | 186 tokens/s | 6.0x faster |
| LocalScore Rating | 405 | 78 | 5.2x higher |
Mac Studio: Maintains functional performance with 1.06-second response time. Suitable for quality-focused applications where enhanced model capabilities justify slower speeds.
Mac Mini M4: Experiences severe degradation with 6.85-second latency. The slow response time makes interactive use impractical for most workflows.
Medium Model (14B Parameters)
| Metric | Mac Studio | Mac Mini M4 | Performance Ratio |
|---|---|---|---|
| Generation Speed | 35.8 tokens/s | 9.6 tokens/s | 3.7x faster |
| Time to First Token | 2,040 ms | 13,300 ms | 6.5x faster |
| Prompt Processing | 583 tokens/s | 96 tokens/s | 6.1x faster |
| LocalScore Rating | 217 | 41 | 5.3x higher |
Mac Studio: Shows significant slowdown with 2.04-second response time. Best suited for batch-oriented workflows where maximum model capability is prioritized over speed.
Mac Mini M4: Performance becomes severely constrained with 13.3-second latency (over 13 seconds before first response). Generation at only 9.6 tokens/s makes this configuration unusable for interactive applications.
Model Scaling Performance
Mac Studio Scaling
| Model Size | Generation | First Token | Prompt Processing | Score |
|---|---|---|---|---|
| 1B (Tiny) | 178 tokens/s | 203 ms | 5,719 tokens/s | 1,713 |
| 8B (Small) | 62.7 tokens/s | 1,060 ms | 1,119 tokens/s | 405 |
| 14B (Medium) | 35.8 tokens/s | 2,040 ms | 583 tokens/s | 217 |
The Mac Studio shows progressive performance degradation as model size increases, but maintains usable performance across all tested sizes. The 8x increase from 1B to 8B parameters results in 65% slower generation, while the 14B model runs at approximately half the speed of the 8B model.
Mac Mini M4 Scaling
| Model Size | Generation | First Token | Prompt Processing | Score |
|---|---|---|---|---|
| 1B (Tiny) | 77.1 tokens/s | 1,180 ms | 1,111 tokens/s | 417 |
| 8B (Small) | 17.7 tokens/s | 6,850 ms | 186 tokens/s | 78 |
| 14B (Medium) | 9.6 tokens/s | 13,300 ms | 96 tokens/s | 41 |
The Mac Mini M4 experiences catastrophic performance degradation with larger models. Moving from 1B to 8B results in 77% slower generation, and the 14B model suffers an additional 46% reduction. The 13.3-second time to first token with the 14B model represents a nearly unusable configuration for any interactive application.
Configuration Recommendations
| Configuration | Performance Summary | Best For | Recommendation |
|---|---|---|---|
| Mac Studio + 1B | 178 tokens/s, 203ms latency | Real-time coding, content creation, maximum performance | Excellent – Recommended for professional use |
| Mac Studio + 8B | 62.7 tokens/s, 1.06s latency | Enhanced reasoning, quality over speed | Good – Balanced performance and capability |
| Mac Studio + 14B | 35.8 tokens/s, 2.04s latency | Maximum capability, batch workflows | Fair – For users prioritizing model sophistication |
| Mac Mini M4 + 1B | 77.1 tokens/s, 1.18s latency | Budget-conscious, occasional use | Fair – Acceptable for casual users |
| Mac Mini M4 + 8B | 17.7 tokens/s, 6.85s latency | Not recommended for interactive use | Poor – Too slow for most applications |
| Mac Mini M4 + 14B | 9.6 tokens/s, 13.3s latency | Not recommended for any practical use | Poor – Unusable for interactive applications |
Bottom Line
The Mac Studio demonstrates clear superiority across all tested configurations, with performance advantages ranging from 2-6x for tiny models up to 10x for larger models. The system handles tiny models exceptionally well, small models competently, and medium models adequately for users prioritizing capability over speed.
The Mac Mini M4 is only viable for tiny (1B) models, where it provides functional if slower performance. Small (8B) and medium (14B) models push the hardware well beyond practical limits, with response latencies of 6.85 and 13.3 seconds respectively making interactive use frustrating or impossible.
Hardware choice significantly impacts local AI usability. Users should match their investment to their model size requirements: Mac Studio for flexibility across all model sizes, Mac Mini M4 only if committed to tiny models exclusively.
Performance Context: Apple Silicon vs Dedicated GPUs
While these benchmarks demonstrate the Mac Studio’s leadership among Apple Silicon options, it’s important to maintain realistic expectations. Dedicated GPU solutions, particularly the NVIDIA RTX 4090, deliver significantly higher raw performance—often 3-5x faster than the Mac Studio for similar model sizes. Systems built around high-end GPUs can achieve 400+ tokens/s with small models and maintain better performance scaling with larger models.
However, Apple Silicon offers distinct advantages that make it compelling despite lower absolute performance:
- System Integration: All-in-one design without external GPU requirements
- Energy Efficiency: Lower power consumption and heat generation
- Silent Operation: Minimal fan noise compared to high-performance GPUs
- Unified Memory: Efficient memory sharing between CPU and neural processing
- macOS Ecosystem: Seamless integration with macOS applications and workflows
The choice between Apple Silicon and dedicated GPU solutions depends on priorities. Users requiring maximum raw performance should consider GPU-based systems. Those valuing system integration, energy efficiency, noise levels, and macOS compatibility will find Apple Silicon delivers excellent local AI capabilities within its design constraints.
For more benchmark comparisons across different hardware configurations, visit LocalScore AI.
Benchmark Sources
| Hardware | Model | Parameters | Test Link |
|---|---|---|---|
| Mac Studio | Llama 3.2 1B | 1B (Tiny) | Test #1788 |
| Mac Mini M4 | Llama 3.2 1B | 1B (Tiny) | Test #1789 |
| Mac Studio | Llama 3.1 8B | 8B (Small) | Test #1790 |
| Mac Mini M4 | Llama 3.1 8B | 8B (Small) | Test #1791 |
| Mac Studio | Qwen2.5 14B | 14B (Medium) | Test #1792 |
| Mac Mini M4 | Qwen2.5 14B | 14B (Medium) | Test #1793 |
All tests conducted November 13, 2025, using LocalScore AI with Q4_K Medium quantization.
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