VRAM Estimator & Model Compression Simulator

Calculate the GPU VRAM required to run LLMs with different quantization levels.

Can your GPU run that LLM? Our VRAM estimator helps you determine the memory required for inference based on model size, context length, and quantization (FP16, INT8, 4-bit). Stop guessing and plan your local AI setup with confidence.

VRAM Estimator (Model Compression Simulator)

Estimate the GPU VRAM required to run an LLM for inference with different quantization levels.

About This Tool

The VRAM Estimator is a critical utility for developers, researchers, and hobbyists entering the world of local large language models (LLMs). Before downloading a 30GB model file, the first question is always: 'Will this even fit on my GPU?'. This tool answers that question. VRAM (Video RAM) is the high-speed memory on your GPU, and it's the primary bottleneck for running LLMs. This calculator helps you estimate the VRAM needed by breaking it down into its main components: the space for the model weights themselves and the dynamic memory required for the KV cache, which grows with your context length and batch size. Crucially, it also acts as a compression simulator, allowing you to see the dramatic VRAM savings from techniques like FP16, INT8, and 4-bit quantization. By making these concepts tangible, the tool empowers you to make informed decisions about which models you can run, what hardware you might need, and how quantization can unlock performance on your existing setup.

How to Use This Tool

Use the slider to set the size of the language model in billions of parameters.
Select the quantization level you plan to use, from unquantized FP32 down to 4-bit.
Set the maximum context length (sequence length) your model will need to handle.
Set the inference batch size.
Click "Estimate VRAM" to see the total estimated VRAM requirement.
Review the breakdown between model weights and the KV cache to understand memory allocation.

In-Depth Guide

Understanding VRAM Components

When you run an LLM, VRAM is consumed by several components. The largest and most static part is the **Model Weights**. This is the size of the model itself, and it depends on the number of parameters and the data type used to store them (e.g., FP16 takes 2 bytes per parameter). The second major component is the **KV Cache**. This is dynamic memory that stores intermediate attention calculations (keys and values) for every token in the context. Its size is `batch_size * context_length * (some_model_specifics)`, so it grows linearly with your context and batch size. Finally, there's overhead for activations and the framework itself, which is smaller but still important.

How Quantization Works

Quantization is the process of reducing the precision of the model's weights. A standard model might use 32-bit floating-point numbers (FP32) to store each weight. Quantization converts these to a lower-precision format. **FP16** (or BFloat16) uses 16-bit numbers, cutting model size in half. **INT8** uses 8-bit integers, cutting size by 4x. **4-bit** (like NF4 or Q4_K_M) uses only 4 bits per weight on average, offering an ~8x reduction in size. This reduction in the size of the model weights is the single most effective way to reduce VRAM usage.

The Trade-Offs of Compression

There is no free lunch. Reducing precision can lead to a slight degradation in the model's output quality. However, modern quantization techniques (like those found in `llama.cpp` or `bitsandbytes`) are extremely effective, and the quality loss between FP16 and a good 4-bit quantization is often imperceptible for many tasks. For almost all local inference use cases, the VRAM savings and speed improvements are well worth the tiny hit in perplexity.

Batch Size vs. Context Length

Both of these factors increase the size of the KV Cache and thus your VRAM usage. Increasing the batch size is great for throughput (processing more requests at once) but uses more VRAM. Increasing the context length allows the model to 'remember' more of the conversation but also linearly increases VRAM usage. You must find the right balance for your specific hardware and application.