Mac or PC with a graphics card: which is better for local AI?

The two designs make opposite trade-offs. A graphics card has a small pool of extremely fast memory soldered next to a powerful processor. An Apple silicon Mac has one large pool of moderately fast memory shared by everything. Neither wins outright, and the right answer depends on the size of the model you want to run.

The 2024 hardware guide asked whether a large-memory Mac is enough. Two years on, there are better numbers for answering it.

The hardware side by side

The specifications are the makers' own (RTX 5090, RTX PRO 6000, Mac Studio, Apple power figures). Note the card figures cover only the card, and a PC around it draws more.

What the benchmarks show

The most useful Apple numbers come from the llama.cpp project, which keeps a public table with the exact commit and command, so that anyone can repeat the test. It uses LLaMA 7B, an older small model, compressed to 4 bits:

The pattern is plain: more bandwidth brings more speed, though doubling it from Max to Ultra added about 50% here.

For graphics cards, LMSYS published a comparison in October 2025 using gpt-oss-20b in Ollama:

The two tables use different models and software, so do not compare a row in one with a row in the other. What they do show together is the shape of each option. A card's memory is several times faster than a typical shared pool, and a good card is about four times faster than the Spark on the same test. LMSYS's figures are a year old, and it warned that early software could date them.

Reading speed matters more than it first appears. When an agent loads a large codebase, the model has to process all of it before writing a word. On a shared-memory laptop, Microsoft's own test of a 137-billion-parameter model implies about 71 seconds for a cold 64,000-token prompt.

The software gap

For running a model, the gap has closed. Ollama, LM Studio and llama.cpp all work well on a Mac, and Apple's own MLX LM runs models and does LoRA fine-tuning (adjusting an existing model with a small add-on) on Apple silicon.

Beyond that, NVIDIA still leads. vLLM, a server built for many simultaneous users, ships pre-compiled for CUDA and supports AMD through ROCm, while on Apple silicon it relies on vLLM-Metal, a community-maintained plugin. Most training tutorials, research code and new tools assume CUDA. If you plan to train a small model of your own, a Mac will make you solve problems others have not met.

Windows has its own catch: vLLM does not support it natively, so serving on a Windows PC usually means WSL, the Linux layer inside Windows, or a tool such as Ollama instead.

A decision table

Which one should a beginner choose?

Whichever you already own. Both run the same open models with the same free tools, and you learn the same things on either. A Mac with 16 GB or a PC with an 8 GB card is enough to find out whether local AI is useful to you before spending anything.

If you are buying specifically for this, look at your main goal. Choose a Mac if you want a simple, low-power machine that also handles bigger models, and you mostly run them rather than train them. Choose a PC with an NVIDIA card if you also want to train, already use a Windows or Linux desktop, or care most about speed on models that fit. With graphics card prices where they are in late 2026, compare the whole system price for the memory you need, using the dated tiers in the buying guide.