Can I run it?
Can I run Qwen2.5 VL 7B Instruct on Apple M5 Pro?
Runs
Runs fully in unified memory on your Apple M5 Pro at BF16, 8K context, about 11–15 tok/s. Weights 14.2 GB plus 0.44 GB of KV cache — 15.1 GB of 18.0 GB usable. Up to 32K context still fits.
Best quantBF16
Working context8K
Max that fits32K
Speed11–15 tok/s
Weights14.2 GB
KV cache0.44 GB
Total15.1 GB
Usable18.0 GB
Qwen2.5 VL 7B Instruct details · Apple M5 Pro page · How this estimate is made
Run commands
llama.cpp
llama-server -hf unsloth/Qwen2.5-VL-7B-Instruct-GGUF:BF16 -c 8192 -ngl 28
Ollama
ollama run qwen2.5
LM Studio
lms get unsloth/Qwen2.5-VL-7B-Instruct-GGUF # Then load quant BF16
Assumptions
- Estimate, not a benchmark. Decode speed uses a memory-bandwidth model at 55–75% of peak.
- KV cache is FP16. Engine overhead assumes llama.cpp (0.3 GB + 1% of the weights). Ollama is closer to 0.9 GB + 3%, LM Studio closer to 1 GB + 3%.
- BF16 size is the real GGUF file size from Hugging Face.
- Unified memory uses 75% for the GPU by default, and always leaves about 5 GB for the OS.
- Speed is quoted at 8K context, not at the maximum context that still fits.
Larger models that also fit
- Can I run Qwen3.8 27B on Apple M5 Pro? — Tight fit, UD-Q4_K_XL, 9.3–13 tok/s
- Can I run gemma 4 12b it on Apple M5 Pro? — Runs, UD-Q8_K_XL, 10–14 tok/s
- Can I run Qwen3.5 9B on Apple M5 Pro? — Tight fit, BF16, 9.3–13 tok/s
Updated 2026-10-24. Profile on this page is the device default.