text model · DeepSeek-V2 · Windows
Can I run DeepSeek-V2-Lite on 16GB RAM Laptop (CPU/iGPU only)?
No. DeepSeek-V2-Lite needs ~12.2 GB even at Q4_K_M, but 16GB RAM Laptop (CPU/iGPU only) only has ~12 GB usable.
Needs ~12.2 GB even at Q4_K_M, but only ~12 GB is usable.
- Q4_K_M needed
- ~12.2 GB
- Usable on device
- ~12 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 ram
- Usable for weights
- ~12 GB
- Best runtime
- Ollama (llama.cpp backend)
What you can run instead
Run DeepSeek-V2-Lite on other hardware
FAQ
Can 16GB RAM Laptop (CPU/iGPU only) run DeepSeek-V2-Lite?
No. DeepSeek-V2-Lite needs ~12.2 GB even at Q4_K_M, but 16GB RAM Laptop (CPU/iGPU only) only has ~12 GB usable.
How much memory does DeepSeek-V2-Lite need?
16GB RAM Laptop (CPU/iGPU only) 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 Windows?
LM Studio for a simple setup; Ollama (CUDA) for the most speed. AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.
Sources
Memory figures are estimates. See methodology.