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
Side by Side
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 | — | $3,999 |
NVIDIA RTX
| Chip | Memory | Bandwidth | Power | FP16 TFLOPs | Starting Price |
|---|---|---|---|---|---|
| RTX 3090 | 24 GB | 936 GB/s | 350 W | 142 TFLOPs | $1,499 |
| RTX 4070 Ti SUPER | 16 GB | 672 GB/s | 285 W | 88 TFLOPs | $799 |
| RTX 4080 SUPER | 16 GB | 736 GB/s | 320 W | 104 TFLOPs | $999 |
| RTX 4090 | 24 GB | 1,008 GB/s | 450 W | 165 TFLOPs | $1,599 |
| RTX 5070 Ti | 16 GB | 896 GB/s | 300 W | 177 TFLOPs | $749 |
| RTX 5080 | 16 GB | 960 GB/s | 360 W | 225 TFLOPs | $999 |
| RTX 5090 | 32 GB | 1,792 GB/s | 575 W | 419 TFLOPs | $1,999 |
Apple memory is unified between CPU and GPU, so the whole pool is usable for model weights. NVIDIA VRAM is dedicated.
Bandwidth is the biggest single driver of tokens per second when a model fits in memory.
Apple prices are the starting price of a representative machine for that chip. NVIDIA prices are GPU only and do not include the rest of the build.
Throughput Estimator
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 CalculatorReal Builds
Curated Builds on Each Side
These are pre-modeled builds from our workstation builder. Each one lists parts, pricing, and the AI workloads it handles well.
Apple Silicon Builds
2-Node Mac mini M4 Pro Exo Cluster
$5.0KTwo M4 Pro Mac minis running Exo with RDMA over Thunderbolt 5. The most affordable way to run 70B models locally.
4-Node Mac mini M4 Pro Exo Cluster
$10.0KFour M4 Pro Mac minis in full-mesh RDMA. Best-value path to running 235B-class models locally.
2-Node Mac Studio M3 Ultra Exo Cluster
$12.5KTwo M3 Ultra Mac Studios linked via Exo with RDMA over Thunderbolt 5. Runs 235B models at ~21 tok/s, silent and under 540W.
4-Node Mac Studio M3 Ultra Exo Cluster
$24.5KFour M3 Ultra Mac Studios in full-mesh RDMA. Runs DeepSeek V3 671B at ~32 tok/s and trillion-parameter sparse models locally.
NVIDIA RTX Builds
NOVATECH AI Workstation — i9-14900K + RTX 5080 (Pre-built)
$4.0KLiquid-cooled tower with Intel i9-14900K, RTX 5080 16GB, 64GB DDR5-6000, and 2TB NVMe. CUDA-accelerated single-GPU workstation, assembled in the USA.
Origin PC M-CLASS v2 (Pre-built)
$6.4KMid-tower AI workstation with RTX 5090 32GB and Ryzen 9 9950X. Corsair-cooled, 6TB NVMe, ready for local inference out of the box.
Origin PC L-CLASS v2 (Pre-built)
$33.1KFull-tower AI workstation with RTX 6000 Ada 48GB and Threadripper PRO 7995WX 96-core. Enterprise-grade for heavy training and inference.
Which Should You Buy
A 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. |
| You plan to fine-tune or train models, not just run them | NVIDIA RTX | Training tooling is overwhelmingly CUDA-first. Apple Silicon can fine-tune small models with MLX, but the ecosystem is younger. |
| You want one machine that does both work and AI inference | Apple Silicon (M4 Max) | A single MacBook Pro doubles as your laptop and your inference rig. No second machine to manage, no separate power bill. |
Common Questions
For loading model weights, yes, in practice. The Apple GPU can address the entire unified memory pool, so a 70B model that needs 40 GB simply lives in memory and runs. The catch is bandwidth. Apple memory is shared with the CPU and tops out around 800 GB/s on the best chips, while a single RTX 5090 hits 1792 GB/s.
Decode speed for a single user is roughly memory bandwidth divided by model size. NVIDIA RTX cards have wider memory buses and dedicated tensor cores, so they push more bytes per second and finish each token faster. Apple closes some of the gap with MLX optimizations, but bandwidth is the ceiling.
Yes. Tools like Exo split a model across two or more Macs connected by Thunderbolt 5. People have run DeepSeek V3 671B at usable speeds on a four-node Mac Studio M3 Ultra cluster. It works, but the configuration is more involved than plugging in a second GPU.
A pair of used RTX 3090s remains one of the best price-per-VRAM deals for local LLMs. You get 48 GB of fast VRAM for roughly the price of a single new card. The tradeoffs are higher idle power, more noise, and a beefier case and power supply.
Yes, and this is often the deciding factor. CUDA is the default target for almost every model and framework. Apple has MLX, Metal, and great Ollama and llama.cpp support, but anything beyond text generation (image, video, fine-tuning) usually lands on NVIDIA first.
A Mac Studio under sustained load draws around 70 to 100 watts and is effectively silent. A single RTX 5090 draws up to 575 watts on its own, the full system can pull 800+, and the fans are audible. If your inference box sits on your desk, this matters.