For running local LLMs, Apple Silicon has a quiet superpower: unified memory. The GPU can address the entire pool, so a Mac Studio with 128 GB or more can load models that would need several discrete GPUs on a PC. Within the Mac Studio line, the choice comes down to two chips: the M4 Max and the step-up M4 Ultra.
The short answer: the M4 Max suits most local-AI users; the M4 Ultra is for those loading the very largest models or wanting the fastest token rates.
Key takeaways
- Both rely on unified memory — the GPU can use the whole RAM pool to hold models.
- The M4 Ultra is essentially two M4 Max dies fused: roughly double the GPU cores and memory bandwidth.
- The M4 Ultra supports larger maximum memory, letting it hold bigger models than the M4 Max can.
- For LLM inference, the Ultra delivers noticeably higher tokens-per-second because token generation is bandwidth-bound.
- Buy the M4 Max for models up to ~70B quantized; step up to the M4 Ultra for 100B-class models and maximum speed.
At a glance
| Spec | Mac Studio M4 Ultra | Mac Studio M4 Max |
|---|---|---|
| Chip design | Two M4 Max dies (UltraFusion) | Single M4 Max die |
| GPU cores | Up to ~80-core | Up to ~40-core |
| Unified memory | Higher maximum | Up to 128 GB |
| Memory bandwidth | ~2x the M4 Max | ~546 GB/s |
| AI framework | MLX, llama.cpp (Metal) | MLX, llama.cpp (Metal) |
| Power draw | Higher | Lower |
| Price | Premium | More affordable |
Unified memory: the Mac advantage
On a PC, a model must fit in a discrete GPU’s VRAM — 16, 24, or 32 GB. On a Mac, the GPU shares the entire system memory pool. A 128 GB Mac Studio can therefore load models that would require multiple high-end PC GPUs. This is the single reason Apple Silicon is taken seriously for local AI: capacity that PC desktops reach only with expensive multi-GPU builds.
Both the M4 Max and M4 Ultra share this architecture. The difference is how much memory you can configure and how fast the GPU can stream it.
Two dies, double the bandwidth
The M4 Ultra is built with Apple’s UltraFusion packaging — two M4 Max dies joined into one chip. In practice that means roughly double the GPU cores and, crucially, double the memory bandwidth.
Bandwidth is the number that matters most for LLM inference. Token generation is memory-bound: the chip reads the entire model’s weights for every token produced. The M4 Ultra’s wider memory path therefore translates fairly directly into higher tokens-per-second:
| Workload | M4 Ultra | M4 Max |
|---|---|---|
| Llama 3 8B (4-bit, MLX) | Faster | Strong |
| Llama 3 70B (4-bit) | Comfortable, faster t/s | Runs (needs 128 GB), slower |
| 100B-class models | Fits with higher max memory | Limited by 128 GB ceiling |
We avoid quoting exact tokens-per-second here because real results vary widely with quantization, context length, and framework version — but the direction is consistent: the Ultra is meaningfully faster, and on the largest models it is the only one with enough memory.
MLX vs the PC ecosystem
Both chips run the same software stack: Apple’s MLX framework and llama.cpp with the Metal backend. MLX has matured quickly and is now a genuinely good local-inference path on Apple Silicon.
But be clear about the trade-off versus a PC. The Mac excels at inference of large models thanks to memory capacity. It is weaker for training and fine-tuning, where the CUDA ecosystem still dominates and many libraries have no Metal path. If your goal is to run big models locally, a Mac Studio is excellent. If your goal is to train them, a PC with NVIDIA GPUs remains the better tool.
Choose the M4 Ultra if
- You want to run 100B-class models locally
- You want the fastest token rates Apple Silicon offers
- You run long contexts or multiple models at once
Choose the M4 Max if
- Your models are up to ~70B quantized — 128 GB handles them
- You want the better value and lower power draw
- You also want a strong general-purpose creative workstation
Which Mac Studio should you buy?
Decide by the largest model you realistically need. For 8B to 70B quantized models — which covers the overwhelming majority of local-AI use — an M4 Max with 128 GB is capable, efficient, and the better value. Step up to the M4 Ultra only if you specifically intend to run 100B-class models, want the highest possible token rates, or plan to keep several large models resident at once. The Ultra is a specialist’s machine; the Max is the sensible default.
FAQ
Is the M4 Ultra worth it over the M4 Max for AI?
Only if you need to run very large models (100B-class) or want maximum token speed. For models up to ~70B quantized, the M4 Max with 128 GB is capable and far better value.
Why is unified memory good for running LLMs?
Because the GPU can use the entire system RAM pool to hold a model, a Mac avoids the discrete-VRAM limit of PC GPUs. A 128 GB Mac Studio loads models that would need multiple high-end NVIDIA cards.
Can a Mac Studio train AI models?
It can, but it is not its strength. Apple Silicon excels at inference of large models. For training and fine-tuning, NVIDIA’s CUDA ecosystem is far more mature, and many training libraries lack a Metal path.
M4 Max or M4 Ultra for running Llama 3 70B?
Both can run a 70B model quantized, provided the M4 Max is configured with 128 GB. The M4 Ultra does it faster, thanks to roughly double the memory bandwidth.
Verdict
For local AI, the Mac Studio’s appeal is unified memory — and both the M4 Max and M4 Ultra deliver it. The M4 Max with 128 GB is the right choice for most: it runs models up to 70B quantized, sips power, and doubles as a superb creative workstation. The M4 Ultra is the answer when you genuinely need to go bigger or faster — 100B-class models and top token rates. Pick by the size of the models you actually plan to run, not by the name of the chip.