LLM GPU RAM Calculator: Estimate VRAM for Large Language Models

Free online tool to estimate how much GPU memory (VRAM) you need to run LLMs like Llama, Mistral, and Qwen in inference.

What Is the LLM GPU RAM Calculator?

The LLM GPU RAM Calculator is a simple, free web tool that estimates the GPU VRAM (video RAM) required to run large language models (LLMs) for inference—that is, generating text or answering questions, not training. Enter the model size (e.g. 7B, 13B, 70B) and the inference precision (FP32, FP16, INT8, INT4, or INT2), and the calculator returns an estimate of total GPU memory in GB, including model weights and a buffer for KV cache and activations.

Whether you’re choosing a GPU for local LLM inference, planning a server, or comparing quantization options, this calculator helps you quickly check if your hardware can fit a given model.

Why Use a GPU VRAM Calculator for LLMs?

Running LLMs locally or on your own GPU server requires enough VRAM to hold:

1. Model weights – the main bulk of memory, proportional to parameter count and precision.
2. KV cache – grows with context length and batch size.
3. Activations and overhead – temporary memory during inference.

Underestimating VRAM leads to out-of-memory errors; overestimating can make you buy more GPU than you need. The LLM GPU RAM Calculator gives you a ballpark figure in seconds so you can:

• Decide if your current GPU can run a 7B, 13B, or 70B model.
• Compare FP16 vs INT4 (or INT8) to see how much memory quantization saves.
• Plan upgrades or cloud instances before downloading a model.

How It Works

The calculator uses two inputs:

• Model size (billion parameters) – e.g. 7 for a 7B model, 13 for 13B, 70 for 70B. You can use decimals (e.g. 0.5 for a 500M model).
• Inference precision – bytes per parameter: FP32 (4), FP16/BF16 (2), INT8 (1), INT4 (0.5), INT2 (0.25).

Formula:

• Model weights (GB) = model size (billions) × bytes per parameter.
• Total estimated VRAM (GB) = model weights × 1.2 (adds a 20% buffer for KV cache and activations).

Example: a 7B model in FP16 uses about 14 GB for weights (7 × 2). With the 20% buffer, the tool suggests roughly 16.8 GB total—so a 24 GB GPU is a comfortable fit.

Precision Options: FP32, FP16, INT8, INT4, INT2

Different precisions change both memory use and often quality/speed:

The calculator supports all of these. Switching from FP16 to INT4, for example, cuts weight memory by 4×, so a 7B model drops from about 14 GB to about 3.5 GB for weights—making it possible on 8 GB GPUs.

When the Estimate May Differ

Estimates are for inference only (not training). The 20% buffer is a rule of thumb for typical context length and batch size. In practice:

• Long context (e.g. 32K, 128K tokens) increases KV cache; you may need more VRAM than the estimate.
• Larger batch size also increases memory; the tool does not ask for batch size, so treat the result as a minimum for batch size 1 and moderate context.
• Frameworks and optimizations (e.g. FlashAttention, custom kernels) can reduce real usage; the calculator stays conservative.

So use the result as a planning guide: if the tool says ~17 GB, aim for at least a 24 GB card for headroom.

Try the LLM GPU RAM Calculator

Use the calculator on this page: enter your model size and precision, click Calculate Memory, and get instant estimates for Total GPU VRAM and Model Weights in GB. No sign-up, no installation—just a quick check to see how much GPU memory your next LLM needs.