Mac vs PC for local AI: a PC is usually faster, a Mac usually holds the bigger model

Full disclosure: I build Outlier, a local-AI app that only runs on Apple Silicon, so I have a stake in one side of this. I've tried to make the PC case as strongly as I'd want someone to make mine.

One architectural difference drives everything else

A discrete GPU has its own memory: a fixed pool of VRAM on a very wide bus. Apple Silicon has one pool shared by CPU and GPU. MLX, Apple's framework, notes that its arrays live in shared memory and run on either device without a copy. On the PC the model has to fit in VRAM. Spill into system RAM over PCIe and generation collapses. On the Mac, "VRAM" is whatever you bought, minus what macOS is using. So there is no wall at 32 GB, but there is no 1,792 GB/s bus either.

What actually fits

Weights are the floor, not the total. You also need the KV cache, which grows with context, plus the runtime and the OS. Apple's ML research team measured footprints on a 24 GB MacBook Pro: Qwen 8B is 17.46 GB at BF16 and 5.61 GB at 4-bit, Qwen 14B 4-bit is 9.16 GB, GPT-OSS 20B at MXFP4 is 12.08 GB, and a Qwen 30B MoE 4-bit is 17.31 GB.

Here are Outlier's own tiers. Nano (4B) is 2.37 GB and wants 6 GB of RAM. Lite (9B) is 5.04 GB and wants 12 GB. Quick (26B-a4b) is 15.61 GB and wants 16 GB. Core (27B) is 15.13 GB and wants 24 GB. Vision 3.8 (27B) is 15.5 GB and wants 24 GB. Plus (397B-a17b) is 209 GB, and it runs on a 64 GB machine by paging MoE experts off the SSD at ~14 GB resident. Against a 32 GB card, a 4-bit model up to about 30B is comfortable and quick. A 70B isn't. That's the capacity argument.

Speed: bandwidth is the ceiling, and the PC has more of it

Token generation is bound by memory bandwidth, not compute. Apple's research group says so, and its measurements show it. M4 to M5 lifted bandwidth from 120 GB/s to 153 GB/s. Time-to-first-token sped up 3.33×–4.06×. Generation sped up only 1.19×–1.27×.

So compare bandwidth. Apple lists 460 GB/s for the 32-core-GPU M5 Max, 614 GB/s for the 40-core and 1.2 TB/s for M5 Ultra in the Mac Studio; in the MacBook Pro, 307 GB/s for M5 Pro and 614 GB/s for M5 Max. NVIDIA's RTX 5090 spec page gives a 512-bit GDDR7 bus and no bandwidth figure; its RTX PRO 6000 page lists 1,792 GB/s. Call that about 1.5× an M5 Ultra, 2.9× an M5 Max.

Prefill is processing the prompt before the first token, and it widens the gap further. Prefill is compute-bound where generation is bandwidth-bound. A discrete GPU's much larger compute budget helps prefill even more than it helps generation. The M4 to M5 numbers above show how strongly prefill tracks the hardware. Long-context RAG and agent loops live on prefill, so that's the gap you feel. Apple claims M5 processes prompts up to 4× faster than M4.

Thermals, power, and sustained load

NVIDIA rates the RTX 5090 at 575 W for the card alone and recommends a 1,000 W supply for the PC. Apple lists 480 W as maximum continuous draw for an entire Mac Studio. Over hours that's fan noise, room temperature and an electricity bill. There is no real laptop equivalent. A discrete-GPU laptop with useful VRAM is heavy, loud and thermally limited, where a MacBook Pro carries 128 GB on battery.

Price per usable gigabyte, and the upgrade path

Apple charges steeply per memory tier and solders it. Day-one capacity is what you have until you buy a new computer. Getting past 32 GB on the PC isn't cheap either. NVIDIA's workstation page describes an RTX PRO 6000 Blackwell at 96 GB of GDDR7 with ECC and 1,792 GB/s. That equals the Mac Studio's 96 GB base configuration and carries more bandwidth with it, at a professional price. The M5 Ultra configures to 256 GB or 512 GB, which no single card on NVIDIA's pages that I read matches, so against a pro card the Mac's capacity case rests on the top Mac Studio configurations. Against consumer GPUs it is plainer. NVIDIA's own unified-memory box, DGX Spark, has 128 GB but only 273 GB/s, below an entry M5 Max's 460 GB/s. AMD's product page lists the Ryzen AI Max+ 395 with up to 128 GB of 256-bit LPDDR5x.

Apple's buy pages, read 2026-10-19, start the Mac Studio at $2,499 and the MacBook Pro at $1,999 for the 14-inch M5, $2,499 with M5 Pro and $4,099 with M5 Max. NVIDIA raised the DGX Spark price in February 2026 and blamed memory supply constraints, so check live listings. "The PC is cheaper" is worth re-testing, not repeating.

Unified memory vs VRAM, line by line

Where a PC with a discrete GPU wins

These advantages are real. Several are decisive.

• Raw generation speed. 1,792 GB/s against 614 GB/s on an M5 Max is nearly 3×, and still 1.5× against an M5 Ultra's 1.2 TB/s. Any model that fits streams out faster on the PC.
• Prefill. The prompt-processing gap is far bigger than the generation gap, and agentic work is made of prefill.
• The CUDA software moat. The biggest practical advantage. vLLM treats CUDA, ROCm and Intel XPU as first-class. Apple Silicon lives in a separate out-of-tree plugin, vLLM-Metal, published on PyPI at 0.1.0 (January 2026), with only 0.3.0 nightly builds on GitHub since. bitsandbytes gives NVIDIA official support and calls Apple Silicon experimental. Its macOS wheel is CPU-only. Unsloth describes Apple/MLX support as in progress. New quantization schemes ship CUDA-first.
• Fine-tuning and training. The LoRA/QLoRA/8-bit-optimizer stack is CUDA kernels. Training on your own data is well-trodden on the PC, newer on the Mac.
• Image, video and diffusion. ComfyUI runs on Apple silicon but documents that path as needing a PyTorch nightly build. NVIDIA is the reference implementation.
• Upgradeability. Start mid-range, upgrade later, or add a second card for more VRAM and compute. No Mac does that.
• Production parity. Linux gives you containerized stacks that mirror what runs on a server.
• A used market at every tier. The Mac path has a hard entry price.

The counterweight: AMD isn't a clean escape. bitsandbytes treats ROCm as experimental too, and its supported-GPU matrix is narrower and more version-sensitive than CUDA's. Driver and toolkit versions are a recurring tax. Ollama documents a 550+ NVIDIA driver requirement plus separate ROCm and Vulkan paths. The Mac is one architecture, one driver.

Which machine for which workload

Build the PC if you want to fine-tune or train, you work with diffusion and video models, your models fit in 32 GB and you want maximum tokens per second, your workload is prefill-heavy, or local dev needs to mirror a Linux/CUDA production target.

Buy the Mac if you need more model than a consumer GPU holds, you want local AI on a laptop and on battery, you care about noise and power at your desk, or you'd rather install an app than maintain a driver stack.

Privacy isn't a differentiator. Both run inference on your own machine, so any privacy claim belongs to the software, not the silicon. Outlier is Apple Silicon only, M1 or later, macOS 15+. There is no Windows, Linux or Intel build. On the PC side, llama.cpp, Ollama and LM Studio have you covered. Pick hardware for the work, then software that runs on it.

If you already own an Apple Silicon Mac, the free tiers below show you what it runs before you pay anything.