Apple Silicon vs RTX for Local AI
A practical, data-driven comparison of Mac unified memory and NVIDIA RTX GPUs for running AI models on your own hardware. Real specs, real throughput estimates, and clear guidance on which side fits your team.
Last updated
The Short Version
Specs That Matter for Local AI
Memory size decides which models fit. Memory bandwidth decides how fast they run. Power and price decide what you actually buy.
Apple Silicon
| Chip | Memory | Bandwidth | Power | FP16 TFLOPs | Starting Price |
|---|---|---|---|---|---|
| M4 Pro | 64 GB | 273 GB/s | 30 W | – | $1,399 |
| M4 Max | 128 GB | 546 GB/s | 55 W | – | $3,199 |
| M3 Max | 128 GB | 400 GB/s | 50 W | – | $3,499 |
| M2 Ultra | 192 GB | 800 GB/s | 70 W | – | $3,999 |
| M3 Ultra | 512 GB | 800 GB/s | 80 W |
How Fast Will It Actually Run?
Pick a model size and quantization. We use the same math the full compatibility calculator uses to estimate decode speed on a representative Mac chip and RTX card.
Apple Silicon
Memory Required
43.4 GB
6.9
tok/s
NVIDIA RTX
Memory Required
43.4 GB
Will not fit
Estimates use bandwidth-based throughput math with a 0.65 efficiency factor for Apple Silicon and 0.70 for discrete GPUs. Real numbers vary by runtime, context length, and prompt.
Open the Full Compatibility CalculatorComplete Systems on Each Side
Desktops and laptops built on the chips compared above, with list prices and the workloads each one suits best.
Apple Silicon Systems
Apple Mac Mini (M4 Pro, 2024)
$2.0KPro-tier Mac Mini in the new compact 5×5-inch design with M4 Pro, up to 14-core CPU, 20-core GPU, and 64GB unified memory at 273 GB/s. First Mac Mini with Thunderbolt 5.
Apple Mac Studio (M4 Max, 2025)
$3.7KThird-gen Mac Studio with M4 Max bringing the world's fastest CPU core, up to 40-core GPU with hardware ray tracing, and up to 128GB unified memory at 546 GB/s. First Mac Studio with Thunderbolt 5.
MacBook Pro 16" M4 Max (2024)
$5.0KApple's 16-inch pro laptop with M4 Max chip, up to 128GB unified memory, Liquid Retina XDR display, and up to 24-hour battery life. The benchmark for local LLM inference on a laptop.
MacBook Pro 14-inch M4 Max (2024)
$5.1KA Plain-English Decision Guide
Match your situation to a row. The recommendation reflects what the math and the real-world tradeoffs point to, not a marketing pitch.
| Your Situation | Recommended | Why |
|---|---|---|
| You want to run a 70B model at home with reasonable speed | NVIDIA RTX (dual 3090 or 4090) | Two used 3090s give you 48 GB of VRAM and far more bandwidth than any Mac in the same price range. Inference will feel noticeably faster. |
| You need to load very large models (120B+) on one box | Apple Silicon (M3 Ultra) | A Mac Studio with 256 or 512 GB of unified memory can hold models that simply do not fit on consumer NVIDIA cards. Throughput is lower, but the model loads. |
| Quiet office, low power draw, mostly chat and coding | Apple Silicon (M4 Pro or M4 Max) | A Mac mini or MacBook Pro is silent, sips power, and runs 7B to 32B models comfortably. Great as a daily-driver developer machine. |
| Image, video, or audio generation as the main workload | NVIDIA RTX | Most diffusion and video models target CUDA first. MPS and MLX support is improving, but RTX is still the path of least resistance. |
