Learn · NVIDIA Hardware & DGX · NVIDIA Graphics Cards for Local AI
RTX Cards vs Macs: The Trade-Off
In plain English
An RTX card is a sprinter: incredibly fast, but it can only carry as much water as its small bucket (VRAM) holds. A Mac is a camel: slower per step, but its giant hump (unified memory) carries enormous models. Sprinters win short races; camels cross deserts. 🐪
What is actually going on
RTX 5090-class cards offer ~32GB VRAM and massive memory bandwidth, crushing tokens/sec for models that fit. When a model exceeds VRAM, layers offload to system RAM over PCIe and throughput collapses. Apple unified memory gives the GPU up to 96-192GB of addressable memory, so huge models fit entirely — at lower bandwidth (~546-819 GB/s) and tokens/sec. The same pattern applies inside NVIDIA's own lineup: RTX PRO cards and DGX machines trade bandwidth for capacity too.
Why it matters
Buy hardware for your workload: an RTX card maximizes speed for 7-32B models (coding assistants, RAG bots), while high-capacity machines (Mac Studio, DGX Spark, DGX Station) are how you run 70B-400B+ frontier-scale models locally at all.
Another way to think about it
Ferrari vs. RV. The Ferrari (RTX GPU) wins any commute that fits in the trunk. The RV (unified memory) is the only one that can move your whole house. 🚗
Check yourself
What happens when a model is bigger than your GPU VRAM?
- It auto-compresses
- Layers offload to system RAM and speed plummets
- The model refuses to load, always
- It runs faster to compensate
Show answer
Layers offload to system RAM and speed plummets — Engines split layers between VRAM and RAM, but system RAM is far slower — token generation crawls.
An RTX 5090 generates tokens faster than a Mac on models that fit fully in VRAM.
- True
- False
Show answer
True — For models that fit, RTX bandwidth and tensor cores win on raw tokens/sec. Macs win when the model doesn't fit on the NVIDIA card at all.