If you’re buying hardware to run AI on your own machine, the choice almost always comes down to two camps: an Apple Silicon Mac (M-series chip with unified memory) or a PC with an NVIDIA GPU (a single discrete card with dedicated VRAM). They are not slightly-different versions of the same thing. They make opposite engineering bets. Apple bets on capacity — letting one big pool of memory hold a model far larger than any consumer graphics card can. NVIDIA bets on speed — raw memory bandwidth plus the most mature software stack in machine learning. The right answer depends entirely on whether you care more about what you can run or how fast it runs.
This guide breaks down the trade honestly, with real model sizes, realistic token-per-second ranges, and total cost of ownership — so you can pick a platform you won’t regret in eighteen months.
The core trade: unified memory vs bandwidth + CUDA
On a PC, the model has to fit inside the GPU’s dedicated VRAM. An RTX 4070 has 12 GB. An RTX 4090 has 24 GB. When the model is bigger than the card, the leftover layers spill into system RAM and run on the CPU, which collapses your speed. The GPU’s VRAM is a hard ceiling, and on consumer cards that ceiling tops out at 24 GB (or 32 GB on the RTX 5090). If you’re new to this distinction, our RAM vs VRAM explainer is the fastest way to internalize why it matters so much.
Apple Silicon throws that wall away. The M-series chips use unified memory — a single pool the CPU and GPU share. Buy a Mac with 64 GB of unified memory and the GPU can address most of that 64 GB for a model. A 36 GB model that would be impossible on a 24 GB RTX 4090 loads comfortably on a 48 GB Mac. That is Apple’s superpower: capacity per dollar, and capacity decides which models you can even attempt.
NVIDIA’s counter is two-fold. First, memory bandwidth — the rate at which the chip can stream the model’s weights — is dramatically higher on a high-end GPU. A 4090 moves roughly 1,000 GB/s; even an M4 Max sits around 500 GB/s, and lesser M-chips far below that. Token generation is bandwidth-bound, so this gap translates almost directly into speed. Second, CUDA: NVIDIA’s software ecosystem is the default target for nearly every ML tool, which means fewer compatibility headaches and day-one support for new models and features.
What a Mac runs that no consumer NVIDIA card can
This is the single most underrated fact in local AI. A well-specced Mac runs models that simply will not fit on a consumer NVIDIA GPU at any usable speed.
- 70B-class models (Llama 3.3 70B, Qwen large variants) at a Q4 quantization need roughly 40–48 GB of memory. No single consumer NVIDIA card has that. A Mac with 64 GB or 96 GB of unified memory runs them. See our walkthrough on the VRAM a 70B model actually needs.
- 30B–34B models (Qwen3 32B, Mixtral-style mixtures, 24B–27B dense models) at Q4 want ~18–24 GB. These barely squeeze onto a 24 GB 4090; on a 32 GB+ Mac they fit with comfortable context headroom.
- Long context windows, where the KV cache for a big conversation can eat several extra gigabytes that a 24 GB card doesn’t have to spare.
The practical upshot: if your goal is to run the biggest, smartest open-weight models at home — the ones that genuinely rival hosted services — a 64 GB+ Mac is often the cheapest way to get there. To run a 70B on NVIDIA you typically need two 24 GB cards (e.g. dual used RTX 3090s) and the power supply, motherboard, and cooling to feed them; our 70B-at-home GPU guide covers that build.
What NVIDIA wins: 2–6x faster tokens and a mature ecosystem
Capacity is meaningless if the model crawls. Here NVIDIA dominates.
On a model that fits comfortably in VRAM, a high-end NVIDIA card is typically 2–6x faster in tokens per second than an Apple chip running the same model — and the gap widens at the smaller, popular model sizes (7B–13B) that most people actually use daily. A 4090 chewing through a 13B model feels instantaneous; a mid-range M-chip on the same model feels notably more deliberate. If you don’t know what token speed is actually “fast enough” to feel real, read how many tokens per second is usable.
NVIDIA’s other win is maturity. CUDA is the assumed backend for almost everything: new model architectures, quantization formats, inference tricks like speculative decoding, and image/video generation all land on NVIDIA first. Apple’s MLX framework and Metal support have improved enormously, and Ollama runs beautifully on both platforms, but you will occasionally hit a brand-new model or feature that’s CUDA-only for a few weeks. On NVIDIA you rarely wait.
Real numbers: token speed and model-size ceilings
Treat these as ballpark ranges, not lab benchmarks — actual numbers swing with quantization (Q4_K_M is the common sweet spot), context length, and the specific model. The shape of the comparison is what matters.
| Config | Memory for models | Largest comfortable model (Q4) | Feel on a 7B–13B model |
|---|---|---|---|
| RTX 3060 12 GB | 12 GB VRAM | ~13B | Fast |
| RTX 4090 / 3090 24 GB | 24 GB VRAM | ~30–34B (tight) | Very fast |
| Dual RTX 3090 (48 GB) | 48 GB VRAM | ~70B | Very fast |
| Mac, 16 GB unified | ~10–11 GB usable | ~8–13B | Moderate |
| Mac, 36–48 GB unified | ~28–40 GB usable | ~30–34B | Moderate |
| Mac, 64–128 GB unified | ~50–110 GB usable | 70B and beyond | Moderate, drops on 70B |
The pattern is consistent: NVIDIA wins the speed column at every size it can fit; Apple wins the size column at every price it can reach. A 24 GB NVIDIA card and a 36 GB Mac cost roughly the same all-in, yet they’re good at opposite things — the card runs a 13B model blazingly, the Mac runs a 30B model acceptably. For more granular per-tier picks, our local AI hardware guide maps models to specific VRAM tiers.
Total cost of ownership
Sticker price is only the start. The real cost over a few years also includes electricity, noise, heat, and how long the thing stays useful.
| Factor | Apple Silicon Mac | NVIDIA PC |
|---|---|---|
| Upfront (capable config) | Higher base, but RAM-to-capacity is efficient | A used RTX 3090 build can be cheaper; high-end new builds cost more |
| Idle power | ~5–30 W (sips power) | 50–100+ W idle for a big GPU rig |
| Load power | ~30–90 W under inference | 250–450 W per high-end GPU |
| Noise / heat | Near-silent, runs cool | Fans audible under load, dumps real heat into the room |
| Form factor | Mac mini / laptop, tiny | Mid/full tower, big PSU |
| Longevity | Long support, holds resale value | Upgradeable piece-by-piece (swap the GPU later) |
Two honest takeaways. First, the Mac’s running-cost story is genuinely excellent — a Mac mini for local AI idles at a few watts and is silent, which matters a lot if the machine lives on your desk or runs 24/7. Second, NVIDIA’s upgrade path is its hidden value: you can start with one card and add or swap later, and the used RTX 3090 remains the best value in local AI precisely because 24 GB of fast VRAM at that price is hard to beat. A noisy, power-hungry box in a closet is fine; the same box humming next to your bed is not.
Both run a local companion — you’re picking the platform, not the product
Here’s the reassuring part: for running a private AI on your own machine, the platform choice rarely changes which software you run. Ollama installs and runs the same way on both — the same one-line install (curl -fsSL https://ollama.com/install.sh | sh on macOS/Linux), the same ollama run <model>, the same local API on 127.0.0.1:11434. The same open-weight models, the same quantizations, the same front-ends. The model doesn’t know or care whether bandwidth came from CUDA or unified memory.
That means your decision is about the hardware traits — capacity vs speed, silence vs upgradeability — not about features you’ll gain or lose. A local companion that keeps every message on your own disk works identically on either. The only thing the platform changes is how big a model your companion can think with and how fast it replies.
Verdict: Apple for capacity and silence, NVIDIA for raw speed
- Buy Apple Silicon if you want to run the largest open-weight models (30B–70B) without a multi-GPU build, you value a silent, low-power, tiny machine, and you can accept good-enough token speed rather than blistering speed. The 64 GB+ Mac is the simplest path to running 70B-class models at home.
- Buy NVIDIA if you want the fastest possible responses on the 7B–34B models most people actually use, you want day-one compatibility with every new model and tool, or you want an upgrade path you can grow over time. A 24 GB card is the speed-per-dollar champion; dual 24 GB cards beat a Mac on 70B speed if you’ll tolerate the power and noise.
Neither is “better.” They’re optimized for different priorities. Be honest about which one is yours.
Decision tree by budget and use case
- Tight budget, just want it to work: A single used RTX 3090 (24 GB) or an RTX 3060 12 GB build for smaller models. Fast, cheap, proven. NVIDIA wins value here.
- Mid budget, want a clean quiet machine on your desk: A Mac mini / Studio with 36–48 GB unified memory. Silent, sips power, runs solid 30B-class models.
- Want to run the biggest models, single box: A Mac with 64–128 GB unified memory. The only sane single-machine route to 70B without juggling multiple GPUs.
- Want maximum speed on big models and don’t mind a furnace: A dual-RTX-3090 (48 GB) PC. Fastest 70B at home, loud and power-hungry.
- Already own a gaming PC with a decent NVIDIA card: Start there. You likely already have the best budget AI box you need — just install Ollama and go.
Whichever side you land on, the real win is the same: an AI that runs entirely on hardware you own, with conversations that never leave your machine and no cloud company deciding what it’s allowed to say. If you want that ownership-first experience as a finished companion rather than a parts list, Ember runs 100% locally on the very hardware this guide describes — Apple Silicon or NVIDIA, your machine, your data, paid once.