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Apple M5 Pro (16-core GPU) vs Apple M5 Pro (20-core GPU) for local LLMs
Across 5 tests measured on both, the two are about even (median ratio 0.99×).
On Llama 2 7B (F16, llama-bench) the Apple M5 Pro (16-core GPU) generates 21.4 tokens/s and the Apple M5 Pro (20-core GPU) 21.6 tokens/s (measured).
Same model, same quant, same test
| Model | Quant / test | M5 Pro (16-core GPU) tok/s | M5 Pro (20-core GPU) tok/s | Faster | Sources and run details |
|---|---|---|---|---|---|
| Llama 2 7B | F16 llama-bench | 21.4 Metal | 21.6 Metal | even | llama.cpp #4167 M5 Pro (16-core GPU) · llama.cpp #4167 M5 Pro (20-core GPU) |
| Llama 2 7B | Q4_0 llama-bench | 67.6 Metal | 66.3 Metal | even | llama.cpp #4167 M5 Pro (16-core GPU) · llama.cpp #4167 M5 Pro (20-core GPU) |
| Llama 2 7B | Q8_0 llama-bench | 38.9 Metal | 38.9 Metal | even | llama.cpp #4167 M5 Pro (16-core GPU) · llama.cpp #4167 M5 Pro (20-core GPU) |
| Llama 3.1 8B Instruct | Q4_K_M LocalScore | 43.5 llamafile | 44.7 llamafile | even | LocalScore M5 Pro (16-core GPU) · LocalScore M5 Pro (20-core GPU) |
| Qwen2.5 14B Instruct | Q4_K_M LocalScore | 22.1 llamafile | 24.0 llamafile | M5 Pro (20-core GPU) +9% | LocalScore M5 Pro (16-core GPU) · LocalScore M5 Pro (20-core GPU) |
Generation speed in tokens/s, best measured backend for each chip (shown under the number). Backends can differ between the two chips; check them before drawing conclusions. Measured results only, nothing estimated.
Specs
| Apple M5 Pro (16-core GPU) | Apple M5 Pro (20-core GPU) | |
|---|---|---|
| Memory | Unified memory | Unified memory |
| Memory bandwidth | 307 GB/s | 307 GB/s |
All results for the Apple M5 Pro (16-core GPU) · All results for the Apple M5 Pro (20-core GPU) · More comparisons