Stop Buying Expensive GPUs? Why Apple Mac Studio Is Changing the Local AI Game

For some buyers, yes. If your goal is to run a large language model for yourself or a small team, a Mac Studio with lots of unified memory now does what used to take one or more expensive high-VRAM cards. Our arithmetic puts its memory at under a third of the cost per GPU-usable gigabyte. But it doesn't make GPUs obsolete. On the same model, an NVIDIA RTX PRO 6000 generated text about 2.5 times faster than an M5 Max and processed prompts two to five times faster. Unified memory changes what fits, not how fast it runs. Whether you can stop buying GPUs depends on which of those two limits your work hits first.

What does a gigabyte of AI memory cost?

Source: SourceIT listings (8 Nov 2026) for the RTX PRO cards; *Mac prices calculated from Apple Singapore's upgrade pricing (confirm on apple.com/sg); Apple specifications; NVIDIA specifications. "Usable" for the Mac assumes macOS's default GPU allowance of about 75% of unified memory (it can be raised with an unsupported setting). Cost per GB is our arithmetic. The RTX cards need a host PC, which adds to their cost.

Why does unified memory change the picture?

A language model runs well only if all its weights sit in fast memory. On a PC that means the GPU's own VRAM, which tops out at 32 GB on an RTX 5090 and 96 GB on an RTX PRO 6000. When a model is bigger than the card, part of it spills into system RAM across PCIe and speed collapses. MacStories saw an RTX 5090 drop to 1.5–4.6 tokens per second once a model no longer fitted.

The Mac Studio's memory is shared by the CPU and GPU, and there is a lot of it: up to 128 GB on the M5 Max and 96, 256 or 512 GB on the M5 Ultra. A 120B-class model at 4-bit (around 60–65 GB), or a 200B-plus mixture-of-experts model, fits in a single quiet box. Matching 256 GB with NVIDIA cards would take three RTX PRO 6000s at S$29,100 each, plus a workstation that can hold them.

So for capacity, the Mac is the cheaper route. That's the core of the "stop buying GPUs" argument, and for single-user inference on large models, it holds up.

Where do GPUs still win clearly?

Speed on models that fit. Hardware Corner ran gpt-oss-120b on an M5 Max (in a MacBook Pro, the same chip as the Mac Studio's) and on an RTX PRO 6000. The NVIDIA card generated 221 tokens per second against 88 at a 4K context, about 2.5 times faster, and processed prompts about 4.9 times faster at 4K and twice as fast at 32K. An RTX 5090 beat the larger M5 Ultra too, when the model fitted in its 32 GB: 59 against 48 tokens per second in MacStories' test.

Long prompts. Prompt processing depends on compute, and NVIDIA's tensor cores still lead. The 5090 reached the first token in 26.4 seconds on a 63K-token prompt, against the M5 Ultra's 42.8. For coding agents or document analysis that send long contexts, this adds up.

Many users at once. GPU serving engines such as vLLM and SGLang batch requests efficiently on CUDA. Mac serving tools are newer: the official vllm-metal plugin arrived in September 2026, and the LM Studio MLX engine had no request batching as of its 0.4.0 release. Our guide to running a company's AI on one Mac goes into this.

Training and fine-tuning. MLX supports LoRA and full fine-tuning, but most training tools (Unsloth, DeepSpeed, bitsandbytes on the GPU) are built for CUDA, and NVIDIA cards have more compute to spend.

Rendering is closer than people expect. In Blender's Open Data benchmark, the M5 Ultra's 80-core GPU scored about 15,350 against about 16,000 for an RTX 5090. For 3D work the gap is small. For AI image and video generation we found no like-for-like Mac vs RTX 5090 timing, so treat claims either way with care.

Unified memory lets you load the model. A fast GPU lets you use it quickly. Buy for whichever of the two your work runs out of first.

So who can stop buying expensive GPUs?

You can, if: you run large open models mainly for yourself or a handful of people; you care more about which model you can run than how fast it answers; your tools work on MLX, llama.cpp, Ollama or LM Studio; and you value low noise and power. An M5 Max with 128 GB, or an M5 Ultra with 256 GB or more, covers this well.

You probably can't, if: you serve many users, run long-context agents all day, fine-tune or train models, generate images or video at volume, or need CUDA because your production systems run on NVIDIA. A GPU workstation stays the better tool, and a 96 GB RTX PRO 6000 handles most single-card jobs.

There is a middle path. NVIDIA's own unified-memory machines (DGX Spark and the ASUS Ascent GX10, 128 GB) give CUDA compatibility at desk-side prices, though at 273 GB/s they generate text more slowly than the Mac. Our comparison of M5 Ultra and GB10 and the follow-up on unified memory versus raw GPU power go deeper. For more on why capacity matters, see why 128 GB of unified memory matters.

Buying in Singapore

SourceIT supplies both routes. The Mac Studio M5 Max 128 GB / 1 TB and the M5 Ultra 256 GB / 2 TB are price on request; ask us for a quotation. For GPU workstations, the RTX PRO 6000 Blackwell Workstation Edition is S$29,100 and the RTX PRO 5000 Blackwell 48 GB is S$14,971.20 (listed 8 Nov 2026); we can quote complete workstations around them. For a CUDA desk-side box, the ASUS Ascent GX10 128 GB / 1 TB is S$10,350. RTX 5090 cards are not in stock at the time of writing; ask us about availability. Prices change, so ask for a current quotation. Use Request for Quotation on the product page, call (65) 6978 3502, email Sales@sourceit.com.sg or use our contact page.

Frequently asked questions

Is unified memory the same as VRAM? Not quite. It's shared between the CPU and GPU, so the GPU can use most of it, about 75% by default on macOS. It's slower than the GDDR7 on a high-end NVIDIA card, but there is much more of it.

Can a Mac Studio replace an RTX PRO 6000? For running large models for one user, often yes, at lower cost per GB. For speed, serving many users, long prompts or training, the RTX PRO 6000 is still much faster.

How much slower is a Mac than an NVIDIA GPU? In Hardware Corner's gpt-oss-120b test, an RTX PRO 6000 generated text about 2.5 times faster than an M5 Max and processed prompts two to five times faster.

Why does an RTX 5090 slow down on big models? When a model is larger than its 32 GB, part of it has to sit in system RAM across PCIe. MacStories measured 1.5–4.6 tokens per second in that situation.

Which Mac Studio has enough memory for local AI? 128 GB (M5 Max) handles most 70B–120B models at 4-bit. 256 GB or 512 GB (M5 Ultra) suits 200B-plus mixture-of-experts models or long contexts.