text model · DeepSeek-V2 · macOS
Can I run DeepSeek-V2-Lite on Apple M5 Pro (48GB)?
Yes. DeepSeek-V2-Lite runs on Apple M5 Pro (48GB) at Q4_K_M (~12.2 GB of ~32 GB usable).
Runs at Q4_K_M using ~12.2 GB of ~32 GB usable. You have room for Q8_0 for higher quality.
- Q4_K_M needed
- ~12.2 GB
- Usable on device
- ~32 GB
- Device memory
- 48 GB
- Best quant
- Q4_K_M
Run it
Pick your tool. All three 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 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.
- 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
- 48 GB unified
- Usable for weights
- ~32 GB
- Best runtime
- MLX direct / Ollama (MLX backend)
You could also run
Run DeepSeek-V2-Lite on other hardware
FAQ
Can Apple M5 Pro (48GB) run DeepSeek-V2-Lite?
Yes. DeepSeek-V2-Lite runs on Apple M5 Pro (48GB) at Q4_K_M (~12.2 GB of ~32 GB usable).
How much memory does DeepSeek-V2-Lite need?
Apple M5 Pro (48GB) 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.
Sources
Memory figures are estimates. See methodology.