Hardware comparison
Mac mini M6 (32GB) vs NVIDIA RTX 4080 (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 | Mac mini M6 (32GB) | NVIDIA RTX 4080 (16GB) |
|---|---|---|
| Memory | 32.0 GB (better for this comparison) | 16.0 GB |
| Bandwidth | 170 GB/s | 717 GB/s (better for this comparison) |
| Price (approx) | $1,299Complete system | $1,549GPU 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 8–13 tok/s | Gemma 4 31BQ2_K · estimated 37–55 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 Mac mini M6 (32GB) runs 3 more of the tracked models (51 vs 48), thanks to its 32.0 GB of memory. The NVIDIA RTX 4080 (16GB) has more memory bandwidth (717 GB/s). Higher bandwidth can help generation speed, but model, quant, architecture and runtime also affect the estimates.