Mac Studio M4 Max vs M4 Ultra for AI in 2026: Which to Buy for Local LLMs

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.

At a glance

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 quão rápido 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:

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 inferência 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.

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.

How much unified memory do you actually need?

The chip matters less than the memory tier you pick, because on Apple Silicon the model has to fit in unified memory or it does not run at usable speed. A useful rule: macOS reserves a slice of RAM for the system, so plan on roughly 70-75% of your unified memory being available for the model. The rest goes to the OS, your apps, and the key-value cache that grows with context length. Size up from there, not down.

Work backwards from the model and quantization you intend to run. At a common 4-bit quant, a model needs roughly half a gigabyte of memory per billion parameters, plus headroom for context. That gives a practical buying ladder:

• 36-64GB (M4 Max): comfortable for 7B-14B models at full speed and 30B-class models at 4-bit. Ideal for coding assistants, RAG, and everyday local chat.
• 128GB (M4 Max top spec) or 96GB (M3 Ultra base): the sweet spot for 70B models like Llama 3.3 70B at 4-bit, with room for long context. This is where most serious local-LLM users land.
• 256GB (M3 Ultra): runs multiple large models at once, or a single 70B at higher precision for better quality.
• 512GB (M3 Ultra only): the headline tier. It is the one configuration that can load a 671B Mixture-of-Experts model such as DeepSeek R1 at 4-bit locally, which needs roughly 400GB-plus of memory allocated to the GPU.

Two honest caveats. First, fitting a model is not the same as running it fast: memory bandwidth and the active-parameter count, not total RAM, set your tokens-per-second. A dense 70B will feel noticeably slower than a sparse MoE that activates only a few billion parameters per token. Second, unified memory is soldered and cannot be upgraded later, so buy for the largest model you realistically expect to run over the machine’s life. Under-buying memory is the single most common, and most expensive, mistake Mac Studio AI buyers make.

Perguntas frequentes

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.

Wait, does an M4 Ultra Mac Studio actually exist?

Not as of mid-2026. When Apple refreshed the Mac Studio in March 2025 it paired the M4 Max with an M3 Ultra, not an M4 Ultra, and never shipped an Ultra-tier M4. So the real-world choice is M4 Max versus M3 Ultra. If you are reading “M4 Ultra” in older buying guides, mentally substitute M3 Ultra: it is the chip that scales to 32 CPU cores, 80 GPU cores, 819GB/s of bandwidth, and up to 512GB of unified memory. A true next-generation Ultra is expected with the M5 Mac Studio, widely rumored for later in 2026.

What does it cost to run a Mac Studio for AI compared to a PC GPU rig?

Far less in electricity. An M3 Ultra Mac Studio idles well under 20W and stays under 200W even while serving a huge model like DeepSeek R1, against a PSU rated for roughly 480W. A multi-GPU PC built to hold a comparable model in VRAM can pull several times that under load, plus added cooling. Over years of always-on local inference, the Mac’s efficiency meaningfully offsets its higher purchase price, plus it runs near-silent and needs no special power circuit.

Is the Mac Studio’s memory bandwidth enough for fast local inference?

For single-user local use, yes. Token generation is bandwidth-bound, and the M4 Max delivers up to 546GB/s while the M3 Ultra roughly doubles that at 819GB/s. That is why the Ultra feels markedly faster on large dense models even when both chips can hold the weights. Where Apple Silicon still trails high-end discrete GPUs is raw prompt-processing (prefill) throughput and concurrent multi-user serving, neither of which most desktop AI workflows are bottlenecked on.

Verdict

For local AI, the Mac Studio’s appeal is unified memory — and both the M4 Max e 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.