text model · DeepSeek-V2 · macOS
Can I run DeepSeek-V2-Lite on Apple M4 (24GB)?
Yes. DeepSeek-V2-Lite runs on Apple M4 (24GB) at Q4_K_M (~12.2 GB of ~16 GB usable).
Runs at Q4_K_M using ~12.2 GB of ~16 GB usable.
That figure is at a 4k context and moves about ±15% as context length changes. Apple M4 (24GB) leaves ~3.8 GB of headroom.
- Q4_K_M needed
- ~12.2 GB
- Usable on device
- ~16 GB
- Device memory
- 24 GB
- Best quant
- Q4_K_M
Which quant fits
Run it
Pick your tool. All 3 load the same Q4_K_M weights.
ollama run deepseek-v2:16b llama-cli -hf mradermacher/DeepSeek-V2-Lite-GGUF:Q4_K_M lms get mradermacher/DeepSeek-V2-Lite-GGUF - Parameters
- 16B (MoE, 2.4B active)
- Q4_K_M size
- 10.4 GB
- Q8_0 size
- 16.8 GB
- Context
- 32k
- Ollama tag
- deepseek-v2:16b
- Memory
- 24 GB unified
- Usable for weights
- ~16 GB
- Power draw
- ~65 W
- Best runtime
- Ollama (MLX backend, preview) / MLX direct
You could also run
Run DeepSeek-V2-Lite on other hardware
FAQ
Can Apple M4 (24GB) run DeepSeek-V2-Lite?
Yes. DeepSeek-V2-Lite runs on Apple M4 (24GB) at Q4_K_M (~12.2 GB of ~16 GB usable).
How much memory does DeepSeek-V2-Lite need?
Apple M4 (24GB) has room to spare. At Q4_K_M the weights are ~10.4 GB; with KV cache and runtime overhead, budget ~12.2 GB at a 4k context. It is a Mixture-of-Experts model (16B total / 2.4B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run DeepSeek-V2-Lite 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-v2-lite/apple-m4-24gb) 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.