Can I run it?
Can I run DeepSeek R1 0528 Qwen3 8B on Apple M4 Pro?
Tight fit
Tight fit fully in unified memory on your Apple M4 Pro at BF16, 8K context, about 8.5–12 tok/s. Weights 15.3 GB plus 1.13 GB of KV cache — 16.8 GB of 18.0 GB usable.
Best quantBF16
Working context8K
Max that fits8K
Speed8.5–12 tok/s
Weights15.3 GB
KV cache1.13 GB
Total16.8 GB
Usable18.0 GB
DeepSeek R1 0528 Qwen3 8B details · Apple M4 Pro page · How this estimate is made
Run commands
llama.cpp
llama-server -hf unsloth/DeepSeek-R1-0528-Qwen3-8B-GGUF:BF16 -c 8192 -ngl 36
No matching name in the Ollama library snapshot.
LM Studio
lms get unsloth/DeepSeek-R1-0528-Qwen3-8B-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 2026-10-17. Profile on this page is the device default.