Hardware comparison
MacBook Air/Pro M5 (16GB) vs NVIDIA RTX 3090 (24GB) for local LLMs
How the two stack up for running open-source LLMs locally — memory, bandwidth, price, and how many of the 69 tracked models each one can run.
Scroll sideways to compare both machines. Keyboard: focus the table, then use the arrow keys. Metric names stay visible.
| Hardware | MacBook Air/Pro M5 (16GB) | NVIDIA RTX 3090 (24GB) |
|---|---|---|
| Memory | 16.0 GB | 24.0 GB (better for this comparison) |
| Bandwidth | 153 GB/s | 936 GB/s (better for this comparison) |
| Price (approx) | $1,099Complete system | $1,099GPU only · host PC required, sold separately |
| LLMs it runs | 30 of 69 | 51 of 69 (better for this comparison) |
| Best model it runs | Mistral Small 3.2 24BQ2_K · estimated 10–15 tok/s | Gemma 4 31BQ3_K_M · estimated 39–58 tok/s |
These prices cover different purchases: a GPU card excludes its required host PC; a complete system includes the machine. No price winner is marked. Prices are approximate catalog estimates; check current configurations and prices before buying.
The recommended models and quants differ. These estimated speeds are not a like-for-like comparison. Estimates use 4,096 context tokens, llama.cpp and one stream.
The NVIDIA RTX 3090 (24GB) runs 21 more of the tracked models (51 vs 30), thanks to its 24.0 GB of memory. The NVIDIA RTX 3090 (24GB) has more memory bandwidth (936 GB/s). Higher bandwidth can help generation speed, but model, quant, architecture and runtime also affect the estimates.