GPU Memory Calculator
Calculate GPU VRAM requirements for running large language models. Estimate memory for model weights, KV cache, and activations. Find which GPUs can run your AI model with our comprehensive memory calculator.
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How Much VRAM Do You Need to Run an LLM?
Running AI models locally requires knowing your GPU memory requirements. Our GPU Memory Calculator estimates the VRAM needed for any large language model based on parameter count, precision, batch size, and context length. Find out if your GPU can run Llama, Mistral, or other popular models.
Understanding GPU Memory for LLMs
GPU memory (VRAM) is consumed by three main components: model weights (parameters × bytes per parameter), KV cache (scales with context length × batch size), and activation memory (temporary computation storage). The total determines which GPU can run your model.
VRAM Calculation Formula
VRAM = Model Weights + KV Cache + Activations + OverheadWhy Calculate GPU Memory Requirements?
Choose the Right GPU
Know exactly whether your RTX 3090, A100, or consumer GPU can run a specific model before you buy or rent.
Optimize with Quantization
See how INT8 or INT4 quantization reduces memory requirements, enabling larger models on smaller GPUs.
Plan for Context Length
KV cache grows linearly with context. Calculate if you can support 4K, 8K, or 32K context windows.
Scale Batch Size
Larger batches improve throughput but need more memory. Find your optimal batch size for available VRAM.
How to Use This Calculator
Frequently Asked Questions
A 7B model needs approximately: 28GB at FP32, 14GB at FP16/BF16, 7GB at INT8, or 3.5GB at INT4. Add 1-4GB for KV cache depending on context length and batch size. In practice, a 16GB GPU like RTX 4080 can run 7B models at FP16 with 4K context.