Buyer's guide / AI PCs
Mac vs PC for running local LLMs (2026)
It's not really Mac vs PC — it's capacity vs speed. A Mac's unified memory fits models a PC GPU can't hold; a PC GPU runs the models that fit far faster. Here's how to pick.
By Evan Cole · Last updated July 17, 2026
"Mac or PC for local AI?" is the wrong framing. The real question is which limit you hit first — memory capacity (how big a model you can load) or memory bandwidth (how fast it generates). A Mac wins the first, a discrete-GPU PC wins the second, and a 128 GB unified-memory PC box splits the difference. Once you see it that way, the choice gets easy.
Two philosophies, one tradeoff
The Mac way — unified memory. Apple Silicon shares one large memory pool between CPU and GPU, so a Mac Studio can address up to 512 GB and hold a 100B+ model whole. Its bandwidth (819 GB/s on the M3 Ultra) is the best of any big-capacity machine — but still below a discrete GPU. You get a silent, turnkey box and the macOS/MLX stack; you pay a premium and give up CUDA.
The PC way — discrete GPU. An RTX 5090 (32 GB) or 4090 (24 GB) has roughly twice a Mac's bandwidth (~1-1.8 TB/s), so it generates tokens fastest — on models that fit in its VRAM. Above ~32B you're forced to quantize hard or offload to system RAM (slow). You also get CUDA, the broadest software support, at the cost of a DIY build that runs hotter and louder.
The third way — a 128 GB unified PC box. A Ryzen AI Max+ 395 mini-PC or an NVIDIA GB10 machine gives Mac-like capacity (128 GB unified, fits 70B) at PC prices — the Ryzen box from ~$1,999, the GB10 with the CUDA stack. The trade is bandwidth (~256-273 GB/s), the lowest of the three, so it's the slowest per token.
Head to head
| Mac (Apple Silicon) | PC + discrete GPU | 128 GB unified box | |
|---|---|---|---|
| Max memory for the model | up to 512 GB (M3 Ultra) | 24-32 GB (single GPU) | up to 128 GB unified |
| Memory bandwidth | 819 GB/s (M3 Ultra) | ~1,008-1,792 GB/s* | ~256-273 GB/s* |
| Biggest model at Q4 | 100B+ | ~32B (70B only w/ offload) | 70B |
| Tokens/sec (relative) | Medium-high | Highest | Lowest |
| Ecosystem | macOS + MLX | CUDA (best support) | CUDA (GB10) / ROCm-Vulkan (Ryzen) |
| Setup | Turnkey, silent | DIY, louder/hotter | Turnkey |
| Entry price | $799 (Mac mini) | GPU + full build | ~$1,999 (Ryzen AI Max) |
* Bandwidth not stated on the maker's own page — discrete-GPU figures from the TechPowerup database; the Ryzen AI Max+ 395 figure is computed from its 256-bit LPDDR5x-8000 spec. Apple and NVIDIA GB10 bandwidth are manufacturer-stated.
Which should you buy?
- Fastest speed on models up to ~32B → a PC with a discrete GPU (RTX 5090 for 32 GB, or a used RTX 3090/4090 for value). See the best GPUs guide.
- Run 70B-120B models quietly with zero setup → a Mac Studio (M4 Max, or M3 Ultra up to 512 GB). A Mac mini M4 Pro is the cheapest Apple-Silicon on-ramp. Check Mac mini price on Amazon →
- Run 70B models cheaply → a 128 GB Ryzen AI Max+ 395 box. Check price on Amazon →
- Big models + the CUDA ecosystem → an NVIDIA GB10 box. Check price on Amazon →
Not sure which memory tier your model needs? Plug it into the LLM VRAM calculator, read the full hardware guide, or see ranked picks in the best AI PC guide.
Frequently asked questions
Is a Mac or a PC better for running local LLMs?
Neither wins outright — they optimise different limits. A Mac's unified memory (up to 512 GB on the M3 Ultra) fits far bigger models than a 24-32 GB PC GPU, so it can run 70B-120B models a single GPU can't hold. But a discrete PC GPU has ~2x the memory bandwidth, so it generates tokens faster on models that fit in its VRAM. Pick a Mac to run the biggest models quietly; pick a PC GPU for the fastest speed on models up to ~32B; or pick a 128 GB unified PC box (Ryzen AI Max / GB10) for a cheaper middle ground.
Why can a Mac run a 70B model when an RTX 5090 can't?
Capacity. A 70B model at 4-bit needs ~40-48 GB; the RTX 5090 has 32 GB of VRAM, so the model doesn't fully fit and must be quantized harder or offloaded to system RAM (which is slow). A Mac Studio with 128-512 GB of unified memory simply holds the whole model. It runs slower per token than the 5090 would on a model that fits, but it runs the model at all.
Does the Mac's lack of CUDA matter?
Sometimes. CUDA has the broadest software support, so the newest inference tricks and some tools land there first. But mainstream local-LLM runtimes (llama.cpp, Ollama, LM Studio, MLX) all run well on Apple Silicon, so for standard chat/coding/RAG use a Mac is fine. If you need specific CUDA-only libraries or plan to fine-tune with the NVIDIA stack, a CUDA machine (a GPU or a GB10 box) is the safer pick.
Sources
Memory capacities and Apple / NVIDIA GB10 bandwidth are from manufacturer pages: Apple's Mac Studio (M3 Ultra 819 GB/s, up to 512 GB; M4 Max up to 546 GB/s) and NVIDIA's DGX Spark (GB10, 128 GB, 273 GB/s). Discrete-GPU bandwidth is from the TechPowerup database, and the Ryzen AI Max+ 395 figure is computed from its 256-bit LPDDR5x-8000 spec. Bandwidth figures not stated on the maker's own page are labelled above. This page was produced with AI assistance; every confirmed spec was cross-checked against a primary source before publication.
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