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Apple M1 Pro (16-core GPU) vs Apple M2 Pro (16-core GPU) for local LLMs
Across 6 tests measured on both, the Apple M2 Pro (16-core GPU) is faster in 5 and generates a median 1.07× the tokens/s of the Apple M1 Pro (16-core GPU).
On Llama 2 7B (F16, llama-bench) the Apple M1 Pro (16-core GPU) generates 12.8 tokens/s and the Apple M2 Pro (16-core GPU) 12.5 tokens/s (measured).
Same model, same quant, same test
| Model | Quant / test | M1 Pro (16-core GPU) tok/s | M2 Pro (16-core GPU) tok/s | Faster | Sources and run details |
|---|---|---|---|---|---|
| Llama 2 7B | F16 llama-bench | 12.8 Metal | 12.5 Metal | even | llama.cpp #4167 M1 Pro (16-core GPU) · llama.cpp #4167 M2 Pro (16-core GPU) |
| Llama 2 7B | Q4_0 llama-bench | 36.4 Metal | 37.9 Metal | M2 Pro (16-core GPU) +4% | llama.cpp #4167 M1 Pro (16-core GPU) · llama.cpp #4167 M2 Pro (16-core GPU) |
| Llama 2 7B | Q8_0 llama-bench | 22.3 Metal | 22.7 Metal | even | llama.cpp #4167 M1 Pro (16-core GPU) · llama.cpp #4167 M2 Pro (16-core GPU) |
| Llama 3.1 8B Instruct | Q4_K_M LocalScore | 21.8 llamafile | 24.2 llamafile | M2 Pro (16-core GPU) +11% | LocalScore M1 Pro (16-core GPU) · LocalScore M2 Pro (16-core GPU) |
| Llama 3.2 1B Instruct | Q4_K_M LocalScore | 77.8 llamafile | 91.2 llamafile | M2 Pro (16-core GPU) +17% | LocalScore M1 Pro (16-core GPU) · LocalScore M2 Pro (16-core GPU) |
| Qwen2.5 14B Instruct | Q4_K_M LocalScore | 11.8 llamafile | 13.3 llamafile | M2 Pro (16-core GPU) +14% | LocalScore M1 Pro (16-core GPU) · LocalScore M2 Pro (16-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 M1 Pro (16-core GPU) | Apple M2 Pro (16-core GPU) | |
|---|---|---|
| Memory | Unified memory | Unified memory |
| Memory bandwidth | 200 GB/s | 200 GB/s |
All results for the Apple M1 Pro (16-core GPU) · All results for the Apple M2 Pro (16-core GPU) · More comparisons