Text model · DeepSeek-V2
DeepSeek-V2-Lite: RAM and VRAM requirements
DeepSeek-V2-Lite needs about 12.2 GB to run at Q4_K_M (the Q4_K_M GGUF file is ~10.4 GB to download; KV cache and overhead add the rest), or about 18.6 GB at Q8_0. The lightest hardware that runs it is Nvidia GeForce RTX 4060 Ti (16GB).
DeepSeek-V2 family · 16B params (Mixture-of-Experts: activates only 2.4B of 16B params per token, so generation is faster than the total size suggests) · released May 2024 · 537.3K Ollama pulls.
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Q4_K_M and Q8_0 are GGUF download (file) sizes. Memory @ Q4 includes the KV cache and overhead, so it runs larger than the file on disk.
Will it run on your device?
DeepSeek-V2-Lite runs on 17 of 40 tracked devices at Q4_K_M.
Memory breakdown
How context length changes it
Longer context grows the KV cache, which for DeepSeek-V2-Lite is sized by its 2.4B active params, not the full 16B. It needs ~12.2 GB at 4k and ~41.9 GB at 128k.
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
- Apple M1 (8GB) No
- Apple M2 (16GB) No
- Apple M4 (16GB) No
- Apple M5 (16GB) No
- Apple M3 Pro (18GB) No
- Apple M4 (24GB) Yes
- Apple M4 Pro (24GB) Yes
- Apple M5 (32GB) Yes
- Apple M4 Pro (48GB) Yes
- Apple M5 Pro (48GB) Yes
- Apple M4 Max (64GB) Yes
- Apple M4 Max (128GB) Yes
- Apple M5 Max (128GB) Yes
- Apple M3 Ultra (256GB) Yes
RAM-only laptops
iPhone & iPad
Android
NVIDIA GPUs
Same job, different size
Pick by what fits your memory: step down to free up VRAM, or step up if you have headroom.
Similar models
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.
What is the Q4_K_M GGUF file size of DeepSeek-V2-Lite?
The Q4_K_M GGUF file is about 10.4 GB to download, and the Q8_0 GGUF is about 16.8 GB. That is the weights file on disk; to run it you also need room for the KV cache and overhead, so budget ~12.2 GB of memory at Q4_K_M.
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.
Understand the numbers
Short guides to the ideas behind DeepSeek-V2-Lite's memory and quant figures.
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
Last validated 2026-08-03. Memory figures are estimates. See methodology.