Mac or PC for a local LLM? Memory vs speed
A Mac holds bigger models in unified memory; a PC with an NVIDIA GPU generates faster when the model fits. Estimated speeds side by side.
The choice is a trade-off: a Mac gives the GPU a lot of usable memory (unified memory), so bigger models; a PC with an NVIDIA card gives high bandwidth and the CUDA ecosystem, so speed as long as the model fits in VRAM.
Qwen3 32B: fits a 24 GB GPU
| Machine | Bandwidth | Generation speed |
|---|---|---|
| RTX 4070 | 504 GB/s | 2–4 tok/s |
| RTX 3090 | 936 GB/s | 31–42 tok/s |
| RTX 5090 | 1,792 GB/s | 53–77 tok/s |
| Apple M4 Pro (20-core GPU) · 48 GB | 273 GB/s | 8–12 tok/s |
| Apple M4 Max (40-core GPU) · 128 GB | 546 GB/s | 16–24 tok/s |
| Apple M3 Ultra (80-core GPU) · 256 GB | 819 GB/s | 23–37 tok/s |
Qwen3 32B Q4_K_M · estimate · memory bandwidth (… GB/s) and model size
Llama 3.3 70B: beyond a consumer card's VRAM
| Machine | Bandwidth | Generation speed |
|---|---|---|
| RTX 4070 | 504 GB/s | 1 tok/s |
| RTX 3090 | 936 GB/s | 1–2 tok/s |
| RTX 5090 | 1,792 GB/s | 1–3 tok/s |
| Apple M4 Pro (20-core GPU) · 48 GB | 273 GB/s | 3–6 tok/s |
| Apple M4 Max (40-core GPU) · 128 GB | 546 GB/s | 8–11 tok/s |
| Apple M3 Ultra (80-core GPU) · 256 GB | 819 GB/s | 11–17 tok/s |
Llama 3.3 70B Instruct Q4_K_M · estimate · memory bandwidth (… GB/s) and model size
These speeds are bandwidth estimates (low to medium confidence). When the model does not fit in VRAM, part of it runs from system RAM and speed drops: this is where a Mac's unified memory makes the difference. How it works is in the unified memory guide.
In short
Choose a PC with a 24 GB or larger GPU if you target models up to about 32B and fast generation, or use CUDA tools. Choose a Mac with 64 GB or more for 70B models and up, or if you want a quiet, power-efficient machine. For a specific card, see which GPU to buy; for your own case, the configurator.
EstimateData updated 38 days agoSource: Run LLM Locally engine