text model · DeepSeek-R1-Distill · macOS
Can I run DeepSeek-R1-Distill-Qwen 7B on Apple M2 (16GB)?
Yes. DeepSeek-R1-Distill-Qwen 7B runs on Apple M2 (16GB) at Q4_K_M (~6.1 GB of ~10.5 GB usable).
Runs at Q4_K_M using ~6.1 GB of ~10.5 GB usable. You have room for Q8_0 for higher quality.
That figure is at a 4k context and moves about ±15% as context length changes. Apple M2 (16GB) leaves ~4.4 GB of headroom, room to step up to Q8_0 for higher quality.
- Q4_K_M needed
- ~6.1 GB
- Usable on device
- ~10.5 GB
- Device memory
- 16 GB
- Best quant
- Q4_K_M
Which quant fits
Running cost · estimate
- Power draw
- ~50 W
- Electricity / 1M tokens
- ~$0.12
- Pays for itself after
- ~3,155M tok
At ~$0.15/kWh and the estimated ~17 tok/s, a million generated tokens costs about $0.12 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$1,199 Apple M2 (16GB) pays for itself after roughly 3,155 million tokens, so local hardware is mostly a fixed cost, not a per-token one. TDP is the peak draw, so this is an upper bound. Assumptions.
Run it
Pick your tool. All 3 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 - Parameters
- 7B
- Q4_K_M size
- 4.68 GB
- Q8_0 size
- 8.1 GB
- Context
- 128k
- Ollama tag
- deepseek-r1:7b
- Memory
- 16 GB unified
- Usable for weights
- ~10.5 GB
- Power draw
- ~50 W
- Best runtime
- Ollama (llama.cpp Metal backend) / MLX
You could also run
Run DeepSeek-R1-Distill-Qwen 7B on other hardware
FAQ
Can Apple M2 (16GB) run DeepSeek-R1-Distill-Qwen 7B?
Yes. DeepSeek-R1-Distill-Qwen 7B runs on Apple M2 (16GB) at Q4_K_M (~6.1 GB of ~10.5 GB usable).
How much memory does DeepSeek-R1-Distill-Qwen 7B need?
Apple M2 (16GB) 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.
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A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/deepseek-r1-distill-qwen-7b/apple-m2-16gb) Sources
Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.