text model · DeepSeek-R1-Distill · macOS
Can I run DeepSeek-R1-Distill-Llama 8B on Apple M3 Ultra (256GB)?
Yes. DeepSeek-R1-Distill-Llama 8B runs on Apple M3 Ultra (256GB) at Q4_K_M (~6.4 GB of ~192 GB usable).
Runs at Q4_K_M using ~6.4 GB of ~192 GB usable. You have room for FP16 for higher quality.
That figure is at a 4k context and moves about ±15% as context length changes. Apple M3 Ultra (256GB) leaves ~185.6 GB of headroom, room to step up to FP16 for higher quality.
- Q4_K_M needed
- ~6.4 GB
- Usable on device
- ~192 GB
- Device memory
- 256 GB
- Best quant
- Q4_K_M
Which quant fits
Running cost · estimate
- Power draw
- ~270 W
- Electricity / 1M tokens
- ~$0.08
- Pays for itself after
- ~9,521M tok
At ~$0.15/kWh and the estimated ~133 tok/s, a million generated tokens costs about $0.08 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$3,999 Apple M3 Ultra (256GB) pays for itself after roughly 9,521 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
- 256 GB unified
- Usable for weights
- ~192 GB
- Power draw
- ~270 W
- Best runtime
- MLX direct / Ollama (MLX backend)
You could also run
Run DeepSeek-R1-Distill-Llama 8B on other hardware
FAQ
Can Apple M3 Ultra (256GB) run DeepSeek-R1-Distill-Llama 8B?
Yes. DeepSeek-R1-Distill-Llama 8B runs on Apple M3 Ultra (256GB) at Q4_K_M (~6.4 GB of ~192 GB usable).
How much memory does DeepSeek-R1-Distill-Llama 8B need?
Apple M3 Ultra (256GB) 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.
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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-llama-8b/apple-m3-ultra-256gb) 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.