Vantaige LLM VRAM Calculator

Vantaige LLM VRAM Calculator helps you plan local LLM runs by estimating total accelerator memory for a specific open model, quantization, and context length. Instead of relying on rough “parameter count” guesses, it breaks VRAM usage into model weights, the KV cache (which grows with how many tokens you process), and runtime/activation overhead.

You can explore either way: start with a model and choose its quantization format (including GGUF Q4_K_M, Q5_K_M, Q8_0, plus AWQ, EXL2, FP16, and BF16) and a context window (8K up to 128K+), or start with your hardware and filter which models are compatible with it. Fit results include reserved headroom for OS/framework/runtime needs, so “raw VRAM” alone isn’t treated as enough to guarantee a stable run.

The output also includes a clear memory breakdown (weights, KV cache, runtime), an estimated decode speed (token generation rate) based on a bandwidth heuristic, and practical terminal commands for popular local inference setups. If a model doesn’t fit, you’ll see matching hardware options and “rent instead” suggestions for higher-memory setups.

This is best for self-hosters, indie developers, and anyone shopping for an AI GPU who wants to avoid unexpected OOM crashes and choose the right quant/context for their workflow.

What does the VRAM calculator estimate for an LLM run?

It estimates required accelerator memory by combining model weights, the KV cache for your selected context length, and runtime/activation overhead. That total is then checked against your GPU (or Mac unified memory) with a safety headroom reserve.

How does context length change VRAM usage?

Longer context increases the KV cache, which can become the largest VRAM cost at high token windows. A model that fits at 4K can still run out of memory at 32K or 128K.

Does quantization (Q4/Q5/Q8/FP16/BF16) affect VRAM needs?

Yes—quantization changes how much memory the model weights use, which is typically the biggest lever. KV cache memory is affected by the context and assumptions, so you still need the full calculator estimate.

Why might a model OOM even if the file size looks smaller than my VRAM?

Model files mainly represent weights, but GPU memory also has to cover KV cache, activations/runtime overhead, and reserved headroom. Leaving insufficient margin for the actual run can trigger OOM.

Does the calculator determine compatibility using only VRAM capacity?

No. Compatibility includes reserved OS/framework/runtime headroom, so the tool aims to reflect safer “fit” conditions rather than raw capacity alone.

Can I use it for Apple Silicon Macs, not just NVIDIA GPUs?

Yes. You can check compatibility against Apple Silicon unified memory configurations and see how headroom affects whether the run should be practical.