Real spec deltas, benchmark numbers, perf-per-dollar, and a decision matrix.
By Mike Perry, Founder & Editor-in-Chief · Published 2026-04-21 · Updated 2026-09-25 · 2 min read
Apple M3 Ultra vs NVIDIA GeForce RTX 4090 — MSRP, VRAM, TDP, synthetic scores, and real AI inference tok/s head-to-head.
Which models fit on an RTX 4090?
RTX 4090 carries 24 GB of VRAM. The weights column is the range of real Q4_K_M
files on Hugging Face for the models in each size class (about 0.6 GB per billion parameters),
and the runtime plus a usable context window wants about 2 GB on top; every tokens-per-second figure is a
median over community-reported Q4 runs SpecPicks tracks for this card, with the run count and
the source beside it.
Model size
Weights at Q4
Fits in 24 GB?
Measured
Left for context
Source
3B (Llama 3.2 3B, Qwen 3 4B)Runs on almost anything with a discrete GPU, and usably on modern integrated graphics.
20-27B (Gemma 3 27B, Mistral Small)A 27B Q4_K_M file (about 16.7 GB) is more than a 16 GB card holds, so 16 GB means a Q3 quant or partial CPU offload; a 24B loads with a short context. 20 GB holds the class with a usable context window, 24 GB with a long one.
The Apple M3 Ultra and NVIDIA GeForce RTX 4090 often end up on the same shopping shortlist. This head-to-head pulls spec deltas, gaming FPS, AI inference tok/s, and synthetic scores from the live SpecPicks benchmark database, plus a decision matrix at the end.
Specs side by side
Apple M3 Ultra
NVIDIA GeForce RTX 4090
Manufacturer
Apple
NVIDIA
Family
M3
Ada Lovelace
Release year
2025
2022
MSRP
—
$1,599
Boost clock
— GHz
— GHz
L3 cache
— MB
— MB
TDP
— W
450 W
Power and thermals
TDP data pending.
Perf-per-dollar
Full perf-per-dollar analysis pending more benchmark data. Check back as the benchmark DB fills out.
Decision matrix
Get the Apple M3 Ultra if
Get the NVIDIA GeForce RTX 4090 if
You need the most VRAM / cores in the comparison
Budget is tighter
Your workload scales with clock speed
You want better perf-per-dollar
You're on a 2025-era platform anyway
You're keeping an older platform
You prioritize headroom for future larger models
You know exactly what you need today
Don't bother with either if your real bottleneck is somewhere else — at the end of the day, both of these are competent parts. If you're gaming at 1080p, if your LLM workload is a single 7B model, if your renders fit in half this VRAM — get the cheaper part and save the delta.
Bottom line
For most buyers in 2026, the choice between the Apple M3 Ultra and NVIDIA GeForce RTX 4090 comes down to how much headroom you value. If you're certain your workload fits today's requirements, the cheaper card is the rational pick. If you're building a workstation you want to keep relevant for 2-3 years of increasingly hungry models, pay up for the VRAM.
If both the Apple M3 Ultra ($—) and NVIDIA GeForce RTX 4090 ($1599.00) feel overkill, consider the tier below. For gaming at 1440p, an RTX 5070 at $549 or an RX 7900 GRE delivers 80-90% of the experience at less than half the cost — you give up headroom for 4K and some AI/ML work, but not much for modern AAA games.
For AI inference specifically, the cheapest card that holds a 14B q4 model natively in 2026 is the Arc B580 at $249. It's not fast, but it works — and the 12 GB VRAM buys you more headroom than an 8 GB GeForce at the same price.
Get neither if…
Your actual bottleneck is CPU-limited single-threaded software (older games, emulators) — a cheaper GPU paired with a better CPU will outperform both of these in that workload.
You only run 7-8B LLMs and don't plan to go larger — the Apple M3 Ultra and NVIDIA GeForce RTX 4090 are both massively over-provisioned for that use case. An RTX 4070 SUPER will match their tok/s at 7B while costing half as much.
Your workload fits in integrated GPU or unified memory — an Apple M4 Pro 48 GB is $2,399 and holds models neither of these discrete cards can hold.
You can't give the card 1.5x its TDP in clean PSU headroom. Undersized PSUs cause transient shutdowns on transient power spikes; that's not a card problem, it's a build problem.
As an Amazon Associate, SpecPicks earns from qualifying purchases; we also earn on qualifying eBay purchases via the eBay Partner Network. Prices shown were last tracked at crawl time and may vary — check the listing for the current price.
What are the main differences between the Apple M3 Ultra and NVIDIA RTX 4090 for AI workloads?
The Apple M3 Ultra offers advantages in VRAM capacity and unified memory, which can handle larger AI models. The NVIDIA RTX 4090, on the other hand, is often more cost-effective for smaller models and provides better performance-per-dollar in many scenarios. The choice depends on whether your workload benefits more from memory capacity or raw GPU performance.
How does the Apple M3 Ultra compare to the NVIDIA RTX 4090 in gaming performance?
Gaming performance depends on the specific game and resolution. The NVIDIA RTX 4090 generally excels in 4K Ultra settings, offering high frame rates. The Apple M3 Ultra may perform well in optimized macOS games but lacks the same level of support for Windows-based gaming. Synthetic benchmarks suggest the RTX 4090 leads in raw gaming power.
Is the NVIDIA RTX 4090 more power-efficient than the Apple M3 Ultra?
The NVIDIA RTX 4090 has a TDP of 450W, while the Apple M3 Ultra's TDP is not yet specified. Typically, the RTX 4090 draws 70-90% of its rated TDP under sustained load. The Apple M3 Ultra is expected to be more power-efficient due to its architecture, but exact comparisons require real-world data.
What workloads benefit most from the Apple M3 Ultra's unified memory?
Unified memory in the Apple M3 Ultra is particularly beneficial for workloads that require large memory pools, such as training or running large AI models (32B+), video editing, and 3D rendering. It allows seamless memory sharing between the CPU and GPU, reducing bottlenecks in memory-intensive tasks.
Are there budget alternatives to the Apple M3 Ultra and NVIDIA RTX 4090 for AI inference?
Yes, budget alternatives include the NVIDIA RTX 5070 or AMD RX 7900 GRE for gaming and mid-tier AI workloads. For AI inference specifically, the Intel Arc B580 offers 12 GB VRAM at a lower price point, suitable for smaller models like 14B in q4 quantization. These options sacrifice some performance and headroom but are cost-effective for lighter workloads.
