Can I run it?
Can I run Qwen3.8 9B Distill on Apple M3 Pro?
Runs
Runs fully in unified memory on your Apple M3 Pro at Q8_0, 8K context, about 8.2–11 tok/s. Weights 9.11 GB plus 0.27 GB of KV cache — 9.78 GB of 13.0 GB usable. Up to 64K context still fits.
Best quantQ8_0
Working context8K
Max that fits64K
Speed8.2–11 tok/sEstimate from memory bandwidth, not a benchmark. Method.
Weights9.11 GB
KV cache0.27 GB
Total9.78 GB
Usable13.0 GB
Qwen3.8 9B Distill details · Apple M3 Pro page · How this estimate is made
Run commands
llama.cpp
llama-server -hf empero-ai/Qwen3.8-9B-Distill-GGUF:Q8_0 -c 8192 -ngl 32
Ollama
ollama run qwen3.8
LM Studio
lms get empero-ai/Qwen3.8-9B-Distill-GGUF # Then load quant Q8_0
Assumptions
- Estimate from memory bandwidth, not a benchmark. Decode speed uses 55–75% of peak bandwidth.
- 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%.
- Q8_0 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 M3 Pro? — Tight fit, UD-IQ3_S, 6.6–8.9 tok/s (estimate)
- Can I run gemma 4 12b it on Apple M3 Pro? — Tight fit, UD-Q6_K_XL, 5.9–8.1 tok/s (estimate)
- Can I run Qwen3.6 35B A3B on Apple M3 Pro? — Tight fit, UD-Q2_K_XL, 16–22 tok/s (estimate)
Updated 2026-10-24. Profile on this page is the device default.