LLM VRAM Calculator

Data reviewed July 19, 2026 · Methodology

Will that model fit on your GPU? Estimate weights + KV cache + overhead before you download.

Model

Quantization (bits/weight)

Context window (tokens)

KV cache precision

Your GPU VRAM

Operating system

Quick Summary · TL;DR

VRAM needed = model weights + KV cache + compute buffer + ~1-2GB CUDA/OS overhead. Weights = params × bits-per-weight ÷ 8. The KV cache grows with context length, so a long context can cost more than the weights themselves.

What eats your VRAM

Model weights — the biggest chunk; shrinks directly with quantization.
KV cache — scales with context length and grows fast at 32K+ tokens.
Compute buffer — transient working memory for attention.
CUDA + desktop overhead — ~0.6GB CUDA context plus ~1.2GB on Windows.

How to estimate VRAM for a local language model

Model weights are only the starting point. A usable VRAM estimate also needs quantisation, context length, key-value cache, runtime overhead and any layers offloaded to system memory.

LLM GPU memory diagram dividing VRAM between model weights, key-value cache, runtime workspace and safety margin — Weights consume the largest fixed block, while context and runtime overhead grow around them. Leaving a margin helps avoid out-of-memory failures.

Step by step

Enter parameter count and the quantisation format used by the actual model file.
Choose the intended context length; longer prompts and conversations increase key-value cache use.
Add runtime overhead and leave a safety margin for kernels, buffers and the desktop display.
Compare the estimate with usable VRAM, then reduce context or offload layers if it does not fit.

How to read the result

The output estimates memory capacity, not tokens per second or model quality. Two files described as the same bit depth can differ because of quantisation scheme, architecture, vocabulary, cache format and inference software.

Common mistakes to avoid

Calculating parameter count times bits and assuming that weights are the entire memory footprint.
Ignoring key-value cache growth when moving from a short prompt to a long context window.
Using advertised GPU memory as if every byte were available to the model runtime.
Assuming a model that fits will run quickly; memory bandwidth and compute still control speed.

Verify before you buy or change settings

Check the exact model card and file size, then consult the inference runtime’s current memory guidance. Keep system RAM available for partial offload and test with the intended context length. Multi-GPU memory is not always pooled transparently; the runtime and layer split determine what is usable.

Calculator logic and this guide were reviewed July 19, 2026. See the methodology and limitations.

Frequently asked questions

How much VRAM do I need to run an LLM locally?

It depends on the model size, the quantization, and your context length: model weights dominate, then the KV cache grows with context. This calculator adds the real CUDA and OS overhead so the number reflects what actually loads.

What quantization should I use to fit a bigger model?

Q4_K_M is the popular sweet spot, cutting weights to roughly a quarter of FP16 size with minimal quality loss. Dropping to Q3 or Q2 fits even larger models but quality degrades noticeably.

Why does my model need more VRAM than its file size?

Because the weights are only one part; you also pay for the KV cache (which scales with context length), a compute buffer, and around 1-2GB of CUDA and desktop overhead on Windows.

Running local AI on a gaming GPU? VRAM capacity is king — see how the meta builds prioritise it in the Auto PC Builder.