Hardware comparison
MacBook Air/Pro M5 (32GB) vs NVIDIA RTX 5080 (16GB) 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 (32GB) | NVIDIA RTX 5080 (16GB) |
|---|---|---|
| Memory | 32.0 GB (better for this comparison) | 16.0 GB |
| Bandwidth | 153 GB/s | 960 GB/s (better for this comparison) |
| Price (approx) | $1,499Complete system | $1,399GPU only · host PC required, sold separately |
| LLMs it runs | 51 of 69 (better for this comparison) | 48 of 69 |
| Best model it runs | Gemma 4 31BQ2_K · estimated 8–11 tok/s | Gemma 4 31BQ2_K · estimated 48–71 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.
Both estimated speeds use the same model and quant shown above. Estimates use 4,096 context tokens, llama.cpp and one stream.
The MacBook Air/Pro M5 (32GB) runs 3 more of the tracked models (51 vs 48), thanks to its 32.0 GB of memory. The NVIDIA RTX 5080 (16GB) has more memory bandwidth (960 GB/s). Higher bandwidth can help generation speed, but model, quant, architecture and runtime also affect the estimates.