What is VRAM?
The graphics card's own memory, separate from system RAM, and the resource a local model is really trying to fit in.
VRAM is the memory built into a graphics card, separate from the system's RAM, and for local AI it is where the model wants to live. It is smaller than system memory, much faster, and directly wired to the processor that runs the model's math. When a model fits in VRAM, answers come back in seconds; when it does not, the tool splits the work with your CPU and everything slows.
A second, faster memory.
VRAM, video random access memory, is the memory mounted on a graphics card, beside the processor it feeds. Its defining property is bandwidth: the path between the card's processor and its memory is wide and short, which is why generation speed tracks VRAM so closely. System RAM holds more but feeds the CPU through a slower path. This is why a GPU speeds long answers even when the same model technically runs on a CPU: the model's numbers stream from VRAM thousands of times per second during generation.
Why the model wants to live there.
Every token the model writes requires reading the model's weights. If those weights sit in VRAM, the GPU reads them at full speed; if they sit in system RAM, the CPU reads them at a fraction of it. This is why quantization and VRAM are two halves of one decision: fewer bits per weight squeezes a larger model into the same 4 GB. A GTX 1650 class card holds a useful stack of quantized models, which is the whole basis of running research AI on a GTX 1650.
When it does not fit.
Tools handle an oversized model by splitting it: the layers that fit go to the GPU, the rest run on the CPU with system RAM as their workspace. The answer still arrives and its quality is identical; only the speed changes, because the split adds a trip through the slower memory on every token. So the practical question is never whether a model can run but how fast, and the RAM arithmetic is what decides where the split lands.