Can I run it?
Can I run Qwen3 VL 4B Instruct on Apple M4 Pro?
Runs great
Runs great fully in unified memory on your Apple M4 Pro at BF16, 8K context, about 16–22 tok/s. Weights 7.50 GB plus 1.13 GB of KV cache — 9.00 GB of 18.0 GB usable. Up to 64K context still fits.
Best quantBF16
Working context8K
Max that fits64K
Speed16–22 tok/s
Weights7.50 GB
KV cache1.13 GB
Total9.00 GB
Usable18.0 GB
Qwen3 VL 4B Instruct details · Apple M4 Pro page · How this estimate is made
Run commands
llama.cpp
llama-server -hf unsloth/Qwen3-VL-4B-Instruct-GGUF:BF16 -c 8192 -ngl 36
Ollama
ollama run qwen3
LM Studio
lms get unsloth/Qwen3-VL-4B-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 M4 Pro? — Tight fit, UD-Q4_K_XL, 8.3–11 tok/s
- Can I run gemma 4 12b it on Apple M4 Pro? — Runs, UD-Q8_K_XL, 8.9–12 tok/s
- Can I run Qwen3.5 9B on Apple M4 Pro? — Tight fit, BF16, 8.3–11 tok/s
Updated 2026-10-24. Profile on this page is the device default.