DeepSeek V2 Lite VRAM Requirements
Developed by DeepSeek
Find out exactly how much VRAM you need to run DeepSeek V2 Lite locally. Calculate the memory footprint of different GGUF quantization variants (like Q4_K_M or Q8_0), estimate your context length KV cache VRAM footprint, and determine if your hardware supports a full GPU VRAM offload or if you will need to rely on slow partial CPU offloading to avoid a CUDA Out of Memory (OOM) error.
Hardware Configuration
Adjust settings to check compatibility with your system in real time.
Available: 14.50 GB
Available: 29.00 GB
KV Cache & Overhead (2.6 GB) plus 11.9 GB weights fit in GPU VRAM (14.5 GB VRAM used). Remaining 17.4 GB weights are offloaded to System RAM (20.4 GB RAM required with OS).
Requires 31.85 GB total memory (weights: 29.26 GB, context overhead: 0.24 GB, activation overhead: 2.36 GB).
DeepSeek V2 Lite Quantization Formats & VRAM Compatibility
Select a format to set it as active and calculate your system fit dynamically.
| Quant | Weights | KV Cache | Overhead | Total VRAM | Status | Links |
|---|---|---|---|---|---|---|
| Base (Unquantized) | 29.26 GB | 0.24 GB | 2.36 GB | 31.85 GB | CPU Offload | HF weights |
DeepSeek V2 Lite KV Cache Memory Breakdown
How to Setup and Run DeepSeek V2 Lite Locally
Method A: Ollama (Recommended)
Ollama is the easiest way to run models in the background. First, download it from ollama.com, then execute this terminal command:
ollama run <model-name>Method B: LM Studio (GUI)
If you prefer a full graphical interface with chat UI and local server hosting:
- Download and install LM Studio.
- Search for DeepSeek V2 Lite in the home page search tab.
- Select a quantization level (like Q4_K_M) that fits your VRAM, click download, and load it to chat.
Model Specs
- DeveloperDeepSeek
- Parameter Count16B
- Base File Size29.3 GB
- AvailabilityLocal-Only
- Input ModalitiesText
Standard Benchmark Scores
Commercial API Pricing
This model is self-hosted only or official API pricing is not available.
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