Text model · DeepSeek-V2
DeepSeek-V2-Lite requirements
DeepSeek-V2 family · 16B params (Mixture-of-Experts, 2.4B active) · released May 2024 · 537.3K Ollama pulls. Minimum to run at Q4_K_M: Nvidia GeForce RTX 4060 Ti (16GB).
Quantization sizes
| Quantization | Size on disk |
|---|---|
| Q2_K | 6.7 GB est |
| Q3_K_M | 7.8 GB est |
| Q4_K_M (default) | 10.4 GB |
| Q5_K_M | 11.4 GB est |
| Q6_K | 13.1 GB est |
| Q8_0 | 16.8 GB |
| FP16 | 32 GB est |
Lower quant = smaller and faster, slightly lower quality. Q4_K_M is the common default.
Run it
ollama run deepseek-v2:16b llama-cli -hf mradermacher/DeepSeek-V2-Lite-GGUF:Q4_K_M lms get mradermacher/DeepSeek-V2-Lite-GGUF Which devices can run DeepSeek-V2-Lite?
Apple Silicon Macs
RAM-only laptops
iPhone & iPad
Android
NVIDIA GPUs
AMD GPUs
FAQ
How much VRAM or RAM does DeepSeek-V2-Lite need?
At Q4_K_M, DeepSeek-V2-Lite needs about 12.2 GB (weights ~10.4 GB + KV cache + overhead) at a 4k context. At Q8_0 budget ~18.6 GB.
Can DeepSeek-V2-Lite run on a laptop?
DeepSeek-V2-Lite is large; you need a 24 GB+ GPU or a 32-48 GB Mac at Q4_K_M.
Is DeepSeek-V2-Lite cheaper to run because it is a MoE model?
It is faster, not lighter. DeepSeek-V2-Lite activates only 2.4B of 16B params per token (so it runs quickly), but all experts must stay in memory, so it still needs memory for the full 16B.
Can I use DeepSeek-V2-Lite commercially?
Yes. DeepSeek-V2-Lite is licensed DeepSeek License, which permits commercial use.
MoE: 16B total / 2.4B active (2 shared + 64 routed experts per layer, 6 activated per token). Released 2024-05-16. Q4_K_M=10.4GB, Q8_0=16.8GB from mradermacher HF GGUF repo (bartowski does not host this model). Ollama deepseek-v2:16b=8.9GB uses different quantization. Context 32K native. Despite 16B total params, inference speed is close to 2.4B dense models.
Sources
Memory figures are estimates. See methodology.