Hardware comparison
MacBook Air/Pro M5 (16GB) vs NVIDIA RTX 5060 (8GB) 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 5060 (8GB) |
|---|---|---|
| Memory | 16.0 GB (better for this comparison) | 8.0 GB |
| Bandwidth | 153 GB/s | 448 GB/s (better for this comparison) |
| Price (approx) | $1,099Complete system | $449GPU only · host PC required, sold separately |
| LLMs it runs | 30 of 69 (better for this comparison) | 23 of 69 |
| Best model it runs | Mistral Small 3.2 24BQ2_K · estimated 10–15 tok/s | Gemma 3 12BQ2_K · estimated 53–79 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 differ. These estimated speeds are not a like-for-like comparison. Estimates use 4,096 context tokens, llama.cpp and one stream.
The MacBook Air/Pro M5 (16GB) runs 7 more of the tracked models (30 vs 23), thanks to its 16.0 GB of memory. The NVIDIA RTX 5060 (8GB) has more memory bandwidth (448 GB/s). Higher bandwidth can help generation speed, but model, quant, architecture and runtime also affect the estimates.