GPU VRAM Reference: What Each Card Actually Gives You

A VRAM reference for local LLMs: nominal memory of common NVIDIA, AMD, and Apple cards, plus why usable VRAM is always less than the spec sheet says.

GPU VRAM Reference: What Each Card Actually Gives You

When you plan a local model, the first number you need is how much memory your card exposes. This page collects nominal VRAM for common cards across NVIDIA, AMD, and Apple. The critical caveat follows the table: the printed number is not the number your model receives.

Discrete NVIDIA cards

Discrete AMD cards

Apple Silicon unified memory

The Apple rows show nominal unified memory, not the model ceiling; the system reserve lowers the usable figure further.

Integrated graphics

Most integrated GPUs share system RAM and expose little or no dedicated memory, so they are not a fit for anything past tiny models. If you run on integrated graphics you are effectively using CPU plus system RAM, which means CPU offload.

Nominal is not usable

Every card in the table reserves memory before your model sees it. The driver allocates a context, the compute stack holds buffers, and the inference engine keeps graphs and workspaces. On a 24 GB card the model might realistically get a few gigabytes less than 24. The exact shortfall depends on the driver version and the engine, so plan with a margin rather than to the exact spec.

This gap is why the VRAM calculator asks for available VRAM as a separate input instead of assuming the box number. Enter what the model can actually use, not the marketing figure.

How to use this table

1. Find your card’s nominal VRAM.
2. Subtract a few gigabytes for the reserve and engine overhead.
3. Compare that usable figure to the weight size of your target model from the quantization comparison.
4. Add the KV cache for your planned context from the context length cost page.
5. If the sum fits with margin, you are set; if not, lower the quant or pick a smaller model.

Laptop and mobile variants differ

A mobile graphics chip with the same name as a desktop card almost always ships less memory and lower power. The RTX 4090 in a laptop is not the same device as the desktop RTX 4090 and exposes a different memory total. When matching a model to a laptop, read the specific mobile SKU’s memory rather than trusting the shared name. The same caution applies to integrated graphics, which borrow from system RAM and should be treated as CPU-offload hardware, not as a fit card.

Specs can drift

Vendor revisions sometimes change memory on the same model name, and mobile variants almost always differ from desktop ones. The values above are the common nominal figures as of writing; confirm the exact card you own against the manufacturer page before buying or planning around a specific number. This table is a planning aid, not a specification guarantee.