Graphics cards vs Apple silicon for a private AI computer

What decides how well a private AI runs? Mostly where the AI model sits, and how fast the computer can read it. Here is how the main designs compare, including where each one is the better choice.

Three things that matter

• Memory size. The whole model has to fit in memory. A bigger model usually means better answers, but it needs more room.
• Memory speed (bandwidth). After the model is loaded, how fast the chip can read it is a major factor in how quickly it writes an answer.
• Reading speed. When someone asks about a long document, the computer has to read that text first. That depends more on raw computing power, and it is where you notice delays with long documents.

Memory speed, using the makers' own figures

These are the manufacturers' published figures for memory bandwidth, as at October 2026. Higher is faster.

• NVIDIA GeForce RTX 5060 Ti 16GB: 448 GB/s per card, with 16 GB of memory. Our Standard build uses two.
• NVIDIA GeForce RTX 5090: 1,792 GB/s, with 32 GB of memory. Our Workstation build uses one.
• Apple Mac Studio, M5 Max: 460 GB/s, or 614 GB/s in the configuration with the larger graphics section, with up to 128 GB of memory.
• Apple Mac Studio, M5 Ultra: 1.2 TB/s, with up to 512 GB of memory.
• Ordinary desktop memory: roughly 90 GB/s for common dual-channel DDR5-5600 memory (5,600 million transfers a second x 8 bytes x 2 channels).

Bandwidth is not the whole story, because the software and the model also matter. But it shows why a model that fits entirely in a graphics card's memory can answer quickly, and why one that spills over into ordinary system memory slows down sharply. When we size a build, we aim to keep the whole model in graphics memory.

Where graphics cards are strong

• High memory speed on the faster cards, and strong raw computing power, which generally helps with reading long documents.
• Mature software. NVIDIA's CUDA is the most widely supported way to run AI models, so new models and tools tend to work there first.
• Good value for models that fit in the card's memory.

Where unified-memory computers are strong

Apple silicon, and PCs built on a shared pool of memory, use one large pool for the processor and graphics together.

• Size. They can hold much larger models than a single graphics card. Apple lists up to 512 GB on its top Mac Studio. If you need a model far bigger than our 96 GB build can hold, a computer like this may suit you better, and we will say so.
• Quiet, compact and low power. Good for a small space.

The trade-off is that the memory is not as fast as the best graphics cards, and reading a long document can take longer. Results vary with the model and the software, so the only reliable test is your own documents.

What we do

• We size each build to your team and the number of people who will use it at once.
• We name the parts and the AI model in your written quote, and we tell you plainly what the system can and cannot do.
• We test it with several people asking questions together before handover.
• We aim to keep the whole model in graphics memory, rather than letting it spill into slower system memory.
• On our Plus build, the graphics card has 32 GB of error-correcting memory.

Already own a powerful computer, such as a Mac Studio? See setup on a computer you already own.

Read more: What is a local AI? and How much does a private AI computer cost?