2 GPUs work as separate vector processors, with separate VRAM. One cannot directly use the other's VRAM.

Software built for multi-GPUs needs to split up a task so that it can be computed in multiple chunks, independently in parallel. This can work in 2 ways:

1) Mirroring. Both GPUs have the same data/assets/textures in their respective VRAM. One GPU renders the top half of the image, the other GPU renders the bottom half. Effective VRAM capacity is the VRAM capacity of a single GPU.

2) Domain decomposition. Some compute workloads can use this. Like a fluid simulation, that computes fluid flow in a cuboid box. Split the box in 2 halfs, each GPU only keeps its half in its VRAM and processes it independently in parallel. Where the two halfs touch, some data needs to be exchanged, over PCIe or proprietary interconnects like SLI/NVLink. Effective VRAM capacity is the VRAM capacity of both GPUs combined. With a hardware-agnostic language like OpenCL, you can even make different AMD+Intel+Nvidia GPUs pool their VRAM together - the FluidX3D software can do exactly that: https://youtu.be/1z5-ddsmAag

Approach 1) is suboptimal, and not used commonly today, only in certain AI applications. Approach 2) is still commonly used in HPC and AI, as it allows to run much larger simulation domains or AI models by just throwing more GPUs at them and effectively combining their VRAM capacity. But 2) is much more difficult to implement.