The 14-inch with the M5 Pro doubles the base M5's memory bandwidth to 307GB/s and takes up to 64GB, which is what local AI runs on: it generates text from a 7B model about twice as fast as the plain M5 and fits far larger models. The base chip's 16-core GPU renders in Blender at close to twice the plain M5's speed; the 18-core CPU, 20-core GPU option is the one to pick for heavy rendering.
Against the line-up
Blender Benchmark
The whole MacBook Pro line-up side by side, with prices
Every benchmark
Quoted from the publications that ran them; we don't test hardware ourselves. Figures from different publications were run on different systems and scenes, so compare within a row's source.
Local LLM (llama.cpp / Ollama)
| Setting | Result | Source |
|---|---|---|
| Llama 2 7B F16, Generation |
21.4tok/s | llama.cpp community benchmarks (GitHub), Oct 19, 2026 Apple M5 Pro, 16-core GPU, 307GB/s (run on a MacBook Pro 14, 15-core CPU, 24GB) llama-bench tg128 (generating 128 tokens), all layers on the GPU. |
| Llama 2 7B Q4_0, Generation |
67.6tok/s | llama.cpp community benchmarks (GitHub), Oct 19, 2026 Apple M5 Pro, 16-core GPU, 307GB/s (run on a MacBook Pro 14, 15-core CPU, 24GB) llama-bench tg128 (generating 128 tokens), all layers on the GPU. |
| Llama 2 7B Q4_0, Prompt processing |
1,340tok/s | llama.cpp community benchmarks (GitHub), Oct 19, 2026 Apple M5 Pro, 16-core GPU, 307GB/s (run on a MacBook Pro 14, 15-core CPU, 24GB) llama-bench pp512 (reading a 512-token prompt), all layers on the GPU. |
| Llama 2 7B Q8_0, Generation |
38.9tok/s | llama.cpp community benchmarks (GitHub), Oct 19, 2026 Apple M5 Pro, 16-core GPU, 307GB/s (run on a MacBook Pro 14, 15-core CPU, 24GB) llama-bench tg128 (generating 128 tokens), all layers on the GPU. |
Blender Benchmark
| Setting | Result | Source |
|---|---|---|
| Standard run |
3,643samples/min | Blender Open Data, Oct 19, 2026 Apple M5 Pro (GPU - 16 cores), Metal, Blender 5.2.0 Median score over the three scenes, 14 submitted runs. Blender Open Data lists by chip, not by Mac. |
Sources
- Blender Open Data (results database)
- llama.cpp community benchmarks (GitHub) (results database)
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