text model · DeepSeek-R1-Distill · macOS
Can I run DeepSeek-R1-Distill-Qwen 7B on Apple M4 (24GB)?
Yes. DeepSeek-R1-Distill-Qwen 7B runs on Apple M4 (24GB) at Q4_K_M (~6.1 GB of ~16 GB usable).
Runs at Q4_K_M using ~6.1 GB of ~16 GB usable. You have room for FP16 for higher quality.
- Q4_K_M needed
- ~6.1 GB
- Usable on device
- ~16 GB
- Device memory
- 24 GB
- Best quant
- Q4_K_M
Run it
Pick your tool. All three load the same Q4_K_M weights.
ollama run deepseek-r1:7b llama-cli -hf bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M lms get bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF vLLM is NOT a Mac tool, it is a CUDA/Linux serving engine. Unified memory is not a fixed VRAM slice; ~70% is usable for weights.
- Parameters
- 7B
- Q4_K_M size
- 4.68 GB
- Q8_0 size
- 8.1 GB
- Context
- 128k
- Ollama tag
- deepseek-r1:7b
- Memory
- 24 GB unified
- Usable for weights
- ~16 GB
- Best runtime
- Ollama (MLX backend, preview) / MLX direct
You could also run
Run DeepSeek-R1-Distill-Qwen 7B on other hardware
FAQ
Can Apple M4 (24GB) run DeepSeek-R1-Distill-Qwen 7B?
Yes. DeepSeek-R1-Distill-Qwen 7B runs on Apple M4 (24GB) at Q4_K_M (~6.1 GB of ~16 GB usable).
How much memory does DeepSeek-R1-Distill-Qwen 7B need?
Apple M4 (24GB) has room to spare. At Q4_K_M the weights are ~4.68 GB; with KV cache and runtime overhead, budget ~6.1 GB at a 4k context.
What is the best tool to run DeepSeek-R1-Distill-Qwen 7B on macOS?
LM Studio for a simple setup; mlx-lm for the most speed. vLLM is NOT a Mac tool, it is a CUDA/Linux serving engine. Unified memory is not a fixed VRAM slice; ~70% is usable for weights.
Sources
Memory figures are estimates. See methodology.