text model · DeepSeek-R1-Distill · macOS
Can I run DeepSeek-R1-Distill-Llama 8B on Apple M4 Pro (24GB)?
Yes. DeepSeek-R1-Distill-Llama 8B runs on Apple M4 Pro (24GB) at Q4_K_M (~6.4 GB of ~16 GB usable).
Runs at Q4_K_M using ~6.4 GB of ~16 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 M4 Pro (24GB) leaves ~9.6 GB of headroom, room to step up to Q8_0 for higher quality.
- Q4_K_M needed
- ~6.4 GB
- Usable on device
- ~16 GB
- Device memory
- 24 GB
- Best quant
- Q4_K_M
Which quant fits
Running cost · estimate
- Power draw
- ~140 W
- Electricity / 1M tokens
- ~$0.13
- Pays for itself after
- ~5,403M tok
At ~$0.15/kWh and the estimated ~44 tok/s, a million generated tokens costs about $0.13 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$1,999 Apple M4 Pro (24GB) pays for itself after roughly 5,403 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-r1:8b llama-cli -hf bartowski/DeepSeek-R1-Distill-Llama-8B-GGUF:Q4_K_M lms get bartowski/DeepSeek-R1-Distill-Llama-8B-GGUF - Parameters
- 8B
- Q4_K_M size
- 4.92 GB
- Q8_0 size
- 8.54 GB
- Context
- 128k
- Ollama tag
- deepseek-r1:8b
- Memory
- 24 GB unified
- Usable for weights
- ~16 GB
- Power draw
- ~140 W
- Best runtime
- Ollama (MLX backend, preview) / MLX direct
You could also run
Run DeepSeek-R1-Distill-Llama 8B on other hardware
FAQ
Can Apple M4 Pro (24GB) run DeepSeek-R1-Distill-Llama 8B?
Yes. DeepSeek-R1-Distill-Llama 8B runs on Apple M4 Pro (24GB) at Q4_K_M (~6.4 GB of ~16 GB usable).
How much memory does DeepSeek-R1-Distill-Llama 8B need?
Apple M4 Pro (24GB) has room to spare. At Q4_K_M the weights are ~4.92 GB; with KV cache and runtime overhead, budget ~6.4 GB at a 4k context.
What is the best tool to run DeepSeek-R1-Distill-Llama 8B 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.
Embed this
A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/deepseek-r1-distill-llama-8b/apple-m4-pro-24gb) Sources
- apple.com/newsroom/2024/10/apple-introduces-m4-pro-and-m4-max
- apple.com/newsroom/2024/10/new-macbook-pro-features-m4-family-of-chips-and-apple-intelligence
- developer.apple.com
- github.com/deepseek-ai
- github.com/ml-explore
- github.com/raullenchai
- huggingface.co
- lmstudio.ai
- ollama.com
- support.apple.com/en-us/103253
- support.apple.com/en-us/121553
- support.apple.com/en-us/121555
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.