text model · DeepSeek-V4 · macOS
Can I run DeepSeek-V4-Flash on Apple M3 Ultra (256GB)?
Yes. DeepSeek-V4-Flash runs on Apple M3 Ultra (256GB) at Q4_K_M (~179.7 GB of ~192 GB usable).
Fits at Q4_K_M (~179.7 GB of ~192 GB usable) but with little headroom. Close other apps, or drop to a 2k context to free about a gigabyte.
That figure is at a 4k context and moves about ±15% as context length changes. Apple M3 Ultra (256GB) leaves ~12.3 GB of headroom.
- Q4_K_M needed
- ~179.7 GB
- Usable on device
- ~192 GB
- Device memory
- 256 GB
- Best quant
- Q4_K_M
Which quant fits
Run it
Pick your tool. All 2 load the same Q4_K_M weights.
llama-cli -hf teamblobfish/DeepSeek-V4-Flash-GGUF:Q4_K_M lms get teamblobfish/DeepSeek-V4-Flash-GGUF How to run it
On macOS use LM Studio (Polished GUI, ships MLX on Apple Silicon, one-click model downloads.).
- Parameters
- 284B (MoE, 13B active)
- Q4_K_M size
- 174.87 GB
- Q8_0 size
- 302.27 GB
- Context
- 1000k
- Memory
- 256 GB unified
- Usable for weights
- ~192 GB
- Power draw
- ~270 W
- Best runtime
- MLX direct / Ollama (MLX backend)
You could also run
FAQ
Can Apple M3 Ultra (256GB) run DeepSeek-V4-Flash?
Yes. DeepSeek-V4-Flash runs on Apple M3 Ultra (256GB) at Q4_K_M (~179.7 GB of ~192 GB usable).
How much memory does DeepSeek-V4-Flash need?
It is a tight fit on Apple M3 Ultra (256GB). At Q4_K_M the weights are ~174.87 GB; with KV cache and runtime overhead, budget ~179.7 GB at a 4k context. It is a Mixture-of-Experts model (284B total / 13B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run DeepSeek-V4-Flash 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-v4-flash/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.