A $1,000 used GPU ties the $9,499 Mac Studio at the benchmark everyone runs

Put the two numbers side by side and try not to laugh. On Georgi Gerganov's Apple Silicon scoreboard — the llama.cpp maintainer's own commit-pinned benchmark thread — a Mac Studio M3 Ultra generates 92.1 tokens/sec on a 7B model at Q4_0. On hardware-corner's measurements, a used RTX 3090 does 87.5 on Qwen3-8B at Q4_K. Different 7-8B-class models, same llama.cpp harness, same quant class — and a five-year-old card you can buy for about a thousand dollars lands within 5% of a machine that costs $9,499 in its 512GB configuration.

If that were the whole story, this article would be one sentence long. It's not, because the small-model benchmark is precisely the workload where the Mac's one superpower doesn't matter — and where the GPU's one hard limit doesn't either. Everything interesting about this decision lives in what each machine cannot do. I run models on both sides of this fence, and my honest summary is: they're not competitors. They're answers to two different questions.

Two machines built around opposite bets

Generating tokens is a memory-bandwidth job — every token requires reading the model's active weights. Reading your prompt is a compute job. NVIDIA bet on compute with dedicated Tensor Cores and narrow, fast VRAM; Apple bet on an enormous pool of unified memory at moderate bandwidth. Apple's spec sheet gives the M4 Max 546 GB/s and the M3 Ultra 819 GB/s, with up to 128GB and 512GB of that pool usable for models. A 3090 has 936 GB/s and a 4090 1,008 — but forever capped at 24GB.

That's the entire comparison in two sentences: the GPU reads a small library very fast; the Mac reads a huge library at decent speed. Which one you want depends only on the size of the books.

*Generation and prompt-processing figures: M-series from the llama.cpp Apple Silicon scoreboard (LLaMA 7B, Q4_0, commit-pinned); RTX 3090 from hardware-corner.net (Qwen3-8B, Q4_K, CUDA with flash attention). Close model classes, same harness family — treat small differences as noise and large ones as real. We don't run our own benchmarks and both sources disclose their setups, which is why I trust them.

What the Mac does that no consumer GPU can

Hold a genuinely large model in one quiet box. A reviewer cited by TechRadar ran DeepSeek R1 — the full 671-billion-parameter MoE, quantized to 4-bit — entirely in a 512GB M3 Ultra's memory at 17–18 tokens/sec, with the whole machine drawing less than 200W. There is no consumer NVIDIA configuration that does this. Not a 5090, not two of them, not four 3090s on a mining frame. The VRAM simply does not add up to 400GB+ at any sane price or wattage.

But the fine print matters, and this is the part Mac-leaning coverage keeps quiet about. Hardware-corner measured that same class of machine taking on the order of fourteen minutes to process a long prompt into DeepSeek 671B under llama.cpp. Generation speed is livable; prompt processing at scale is the M-series' structural weakness — it's a compute problem, and compute is exactly what Apple didn't prioritize. If your dream workload is 'paste a repo into a huge model locally', the Mac will hold the model and then make you wait like it's 1998. The same tradeoff, in milder form, shows up in the DGX Spark's numbers — big memory, modest bandwidth, patience required.

The quieter advantages are real too. Under €0.30/kWh European electricity — I pay close to that in Denmark — a GPU tower that pulls 350–450W more than a Mac under load costs roughly €25 a month extra if it works an 8-hour day. Over three years that's most of a Mac mini. And 'silent' is not a luxury feature when the machine shares a room with you; a 3090 under sustained inference is a small hairdryer with opinions.

What the NVIDIA box does that no Mac can

• Process prompts at full speed. Even a used 3090 nearly doubles the M3 Ultra's prompt throughput on small models — 2,572 vs 1,471 t/s in the cited measurements — and a 4090 roughly doubles the 3090. For RAG, coding assistants and long-document work, this is the number you feel.
• Fine-tune with the ecosystem's full weight. LoRA/QLoRA tooling, vLLM serving, quantization scripts, image and video generation — it's all CUDA-first, often CUDA-only for months. MLX has made honest progress, and it's still where CUDA was years ago.
• Upgrade. A Mac's memory is decided at checkout, forever. A PC takes a second GPU, more RAM, a bigger card next year.
• Start cheap. The entry ticket to serious local AI is a ~$1,000 used card in a box you may already own — check what fits in 24GB before assuming you need more.

The budget-parity test

Comparisons like this usually dodge the only framing buyers actually face: same money, best machine. So: at ~$2,000, it's a used-3090 PC against a base M4 Max Studio (36GB) — the PC is faster on everything that fits it, the Mac runs somewhat larger models slowly; I'd take the PC unless silence is non-negotiable. At ~$4,000, a base M3 Ultra (96GB) against a dual-3090 build with 48GB — now it's genuinely close, and the deciding question is whether your target models sit above or below 48GB. At $9,500, nothing on the consumer NVIDIA side answers the 512GB Ultra at all; the alternative is not buying a big machine, but renting one by the hour.

Whichever side you land on, size the machine to a real model list, not a vibe. The VRAM calculator tells you what fits in any memory budget, and the cost-to-run pages put live rental prices next to hardware costs so the break-even math is visible before the money leaves. The best local-AI machine is the one whose limits you understood before you bought it.