LLM VRAM Calculator

Our VRAM Calculator simplifies the complex math of running large language models (LLMs) locally. Running an LLM requires enough Video RAM (VRAM) to store the model weights, the context history, and the operational overhead.

You simply select a popular architecture (like Llama 3 or Gemma), choose its base size (e.g., 7B or 13B parameters), and then specify two crucial variables: the quantization level (e.g., Q4_K_M, which reduces precision) and your desired context length.

The tool then calculates the total memory footprint. For instance, running a 7B model at Q4 quantization requires significantly less VRAM than running it unquantized in full precision (FP16). It even models multi-GPU setups, telling you exactly how to distribute the load across multiple cards to ensure smooth local inference.

To maximize your local LLM performance while staying within VRAM limits, focus on balancing model size with optimization techniques.

• Prioritize Quantization: Start with Q4 or even Q3 quantization. This offers the best balance of performance and memory efficiency for most use cases, significantly reducing the VRAM footprint.
• Start Small and Scale Up: If your GPU has limited VRAM (e.g., 8GB), start with highly capable 3B to 7B models. Only move to larger models (13B+) if you have ample multi-GPU resources or a high-end card like the RTX 4090.
• Check Context Needs: If your primary task is summarization, a smaller context window might suffice. If you are building a chatbot requiring long chat history recall, increase the context size gradually and monitor the VRAM usage carefully using this calculator.

Always calculate the requirement for worst-case scenario (maximum expected context) to guarantee stability.