Mac Mini M4 Pro vs Mac Studio M4 Max for Local LLM Inference – Key Considerations

A developer is choosing between two Mac configurations for local LLM inference – both with 64GB unified memory and 1TB storage, both in stock in Switzerland. The two options:

• Mac mini M4 Pro: 12-core CPU / 16-core GPU, 273 GB/s memory bandwidth
• Mac Studio M4 Max: 16-core CPU / 40-core GPU, 546 GB/s memory bandwidth – roughly $600 more

Use case is local inference (no training) with Gemma 4 and Qwen, plus smaller models for agentic workflows, possibly integrated into a VSCode coding harness. The M4 Max clearly wins on paper with double the GPU cores and double the memory bandwidth. But the community asks practical questions:

• Token/s impact: How much does the bandwidth jump (273 → 546 GB/s) affect inference speed for Gemma 4 class models at Q4_K_M or Q5_K_M quantization?
• Prompt processing: For long contexts, is the M4 Pro's 16-core GPU too slow to justify the Max?
• Regret risk: Anyone regret buying the Pro and hitting a performance wall? Or regret paying extra for Max and never using the headroom?

If your inference workload is sensitive to prompt processing latency or you run large models with long contexts, the extra bandwidth may be critical. But $600 is a real price difference – evaluate based on your specific model and context length needs.