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AI VRAM Calculator

Accurately estimate VRAM for local LLM deployment by parameters, quantization and context length

Tool Interface

Interactive tool will be available soon

Features

  • ✓ Estimates for any model from 1B to 400B+ parameters
  • ✓ Supports FP16, BF16, INT8 and INT4 quantization levels
  • ✓ Accurately accounts for KV cache and activation memory
  • ✓ Built-in library of Llama, Qwen, DeepSeek and Mistral models
  • ✓ Recommends minimum and recommended VRAM with matching GPU models

How to Use

  1. Select a model or enter the parameter count
  2. Choose quantization level and context length
  3. Set batch size and inference framework
  4. Click calculate to see VRAM needs and GPU recommendations

FAQ

What is AI VRAM Calculator?

An online VRAM calculator. It estimates the memory needed to deploy large models locally based on parameters, quantization level and context length. Input: model size, quantization, context length. Output: VRAM estimate and GPU recommendations with one-click copy and download. Everything runs in your browser.

How accurate are the results?

Results are based on the industry-standard VRAM formula (weights + optimizer states + KV cache + activations) and cross-checked against llama.cpp, vLLM and Transformers real usage, typically within 10% error.

What is KV Cache and why does it matter?

KV Cache stores key-value pairs during inference to speed up generation. It grows linearly with context length and is a major VRAM cost in long-context scenarios — the calculator accounts for it precisely.

How much VRAM does INT4 quantization save?

Compared with FP16, INT8 roughly halves VRAM and INT4 cuts it by about 75%. Quantization brings slight accuracy loss, so the calculator shows VRAM and recommendations for every precision.

Which inference frameworks are supported?

llama.cpp, vLLM, Transformers and Ollama are supported. VRAM overhead varies slightly by framework, and the calculator adjusts estimates accordingly.