Hardware requirements, VRAM recommendations, and benchmarks for the most popular local AI models.
Llama 3 8B is a powerful lightweight model. To run it efficiently with 4-bit quantization, you need at least 8GB of VRAM. For full precision or longer context windows, 12GB+ is recommended.
Llama 3 70B is a massive model requiring significant memory. You need at least 24GB VRAM (like an RTX 3090/4090) for 4-bit quantization. For better performance, dual GPUs are often required.
DeepSeek Coder V2 is a favorite among developers. Its Mixture-of-Experts (MoE) architecture is efficient but still demands 16GB+ VRAM for smooth code generation and large context windows.
SDXL generates stunning high-resolution images but is VRAM-hungry. While 8GB is the minimum, 16GB VRAM is highly recommended for faster generation and avoiding out-of-memory errors.
Flux.1 is the new king of open-source image generation, delivering midjourney-level quality. It is extremely demanding, requiring at least 12GB VRAM, with 24GB being ideal for the 'Dev' version.
Qwen 2 72B is a top-tier multilingual model. Similar to Llama 3 70B, it requires at least 24GB VRAM for quantized inference, making high-end consumer GPUs or dual-card setups necessary.