Trying to understand VRAM usage and find the sweet spot for Wan/SCAIL-2 (or other models) on a GPU

So upfront I'll admit that this is a ChatGPT summary of my chat with it about this idea i had, but this post wouldn't exist any other way, so... I’m trying to get a better understanding of how VRAM is actually used by Wan/SCAIL-2 workflows in ComfyUI, and whether it’s possible to derive a useful formula for choosing resolution and frame count. My GPU is an RTX 4070 Ti SUPER with 16 GB VRAM, alongside 64 GB system RAM, a Ryzen 5 5600 and an NVMe SSD. From what I understand, the VRAM used by a workflow isn’t just the model itself. It can include model weights, text/image encoders, VAE, latents, activations, attention, temporary tensors and CUDA/PyTorch overhead. The dynamic part should also change with resolution, frame count, batch size, etc. So I’m wondering if the total VRAM usage can be roughly separated into something like: VRAM total = VRAM baseline (loaded models/etc.) + VRAM dynamic (resolution, frames, etc.) For example, if a workflow sits at 9 GB after loading its models and reaches 14 GB during sampling, I’d assume roughly 5 GB is being used by the actual computation. If increasing the frame count raises the peak to 15 GB while the baseline stays around 9 GB, that should give us some idea of how the dynamic part scales. I’m also interested in whether resolution and frame count can be approximated using something like: pixel load = width × height × frames For example, 832×480×81 has about 32.3 million pixel positions, while 1280×720×81 has about 74.6 million, or roughly 2.3× the amount of data. I realize the actual VRAM scaling probably isn’t perfectly linear, especially depending on the model architecture, attention implementation, quantization, VAE, offloading, etc. My idea is to benchmark this rather than guess. For each run I could record: model/workflow resolution frame count sampling steps peak VRAM usage runtime in seconds possibly GPU utilization as well I was thinking of doing around 6–9 tests, keeping everything else constant. For example, vary the frame count at one resolution, then vary the resolution at a fixed frame count. The goal would be to derive two practical models: VRAM: What resolution/frame combinations fit comfortably within 16 GB? Runtime: How does generation time scale with resolution, frames and steps? Ideally, this could lead to something like: VRAM = baseline + f(width, height, frames) and a similar approximation for runtime. I’m mainly interested in finding the practical sweet spot between quality, generation time and VRAM usage, rather than simply pushing the GPU to 15.9/16 GB. Does this approach make sense? And are there better ways to measure the actual VRAM used by the models versus temporary computation? I’d also be interested in knowing whether nvidia-smi, ComfyUI's VRAM reporting, or PyTorch's allocated/reserved memory is the most useful metric for this kind of benchmark. submitted by /u/MoreColors185 [link] [comments]

Source: r/StableDiffusion | 2026-08-01

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