When it comes to running LLMs locally, the real question isn't "which CPU is the fastest?", but how much unified memory you have, at what bandwidth, and with what scaling strategy.
Based on Apple's specifications page, here's the M4 Max vs M3 Ultra matchup from a pure local-AI angle.
Key specs that matter for LLM inference
Mac Studio M4 Max
- 14-core CPU / 32-core GPU (configurable to 16 CPU / 40 GPU)
- 410 GB/s memory bandwidth (up to 546 GB/s depending on config)
- 36 GB unified memory base, configurable up to 128 GB
Mac Studio M3 Ultra
- 28-core CPU / 60-core GPU (configurable to 32 CPU / 80 GPU)
- 819 GB/s memory bandwidth
- 96 GB unified memory base, configurable up to 512 GB
What this means in practice for local models
- M4 Max: excellent for small/mid-sized models, with a more reasonable entry cost.
- M3 Ultra: suited to much heavier models, especially with 4/8-bit quantization and local multi-agent scenarios.
If your goal is a cluster de Mac Studio pour IA of machines, the M4 Max can be a solid entry point. If you're aiming straight for very large models on a single node, the M3 Ultra is the better fit.
Quick decision by profile
- Freelancer / solo dev: a high-end M4 Max with 64/128 GB.
- Local R&D team: 2 to 4 M4 Max nodes in a cluster to iterate fast.
- Heavy on-prem lab: M3 Ultra with 256/512 GB to maximize capacity per node.
Conclusion
The M4 Max is an excellent starting point. The M3 Ultra becomes the reference machine the moment you want to host much larger models with fewer compromises.
In both cases, think architecture before benchmarks: monitoring, network isolation, model versioning and fallback.
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