text model · DeepSeek-V2 · macOS
Can I run DeepSeek-V2-Lite on Apple M4 (16GB)?
No. DeepSeek-V2-Lite needs ~12.2 GB even at Q4_K_M, but Apple M4 (16GB) only has ~10.5 GB usable.
Needs ~12.2 GB even at Q4_K_M, but only ~10.5 GB is usable.
- Q4_K_M needed
- ~12.2 GB
- Usable on device
- ~10.5 GB
- Device memory
- 16 GB
- 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
- 16 GB unified
- Usable for weights
- ~10.5 GB
- Best runtime
- Ollama (MLX backend, preview) / MLX direct
What you can run instead
Run DeepSeek-V2-Lite on other hardware
FAQ
Can Apple M4 (16GB) run DeepSeek-V2-Lite?
No. DeepSeek-V2-Lite needs ~12.2 GB even at Q4_K_M, but Apple M4 (16GB) only has ~10.5 GB usable.
How much memory does DeepSeek-V2-Lite need?
Apple M4 (16GB) does not have enough memory. 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.