GPU workload guide
Which GPU should you rent for AI video?
A workflow-first guide to VRAM, render duration, codecs, storage, and choosing between RTX, L40S, and datacenter GPUs.
By AnchorGPU · Updated · 6 min readChoose from the pipeline, not a model-name tier
Start by recording the exact pipeline: text-to-video, image-to-video, or video-to-video; checkpoint and precision; target width and height; generated frame count; batch size; denoising steps; conditioning inputs; and whether the job includes upscaling or interpolation. Video memory is not determined by parameter count alone. Model weights, attention and temporal activations, latent tensors, and VAE decoding contribute at different stages.
Use the pipeline’s supported dimensions and frame range as constraints. A GPU that loads the checkpoint may still fail on the intended clip, so capacity planning should reproduce the production request rather than a minimal sample.
Frame count and resolution shape the working set
Diffusers notes for Stable Video Diffusion that frames are generated together, resembling a large image batch. More frames and more pixels generally expand intermediate tensors, but the exact curve depends on the architecture and implementation. Frames per second is not a substitute for generated-frame count: export FPS controls playback timing, while num_frames controls how many frames the model creates.
Profile at the real aspect ratio and duration. If a model requires windowed generation or clip stitching, evaluate temporal consistency at boundaries as well as whether the run fits.
Offload and chunking exchange memory for time
Model CPU offload keeps inactive pipeline components in host memory and moves them onto the accelerator when needed. Sequential offload works at a finer level and can save more device memory, but repeated transfers can be much slower. Forward chunking reduces the amount of feed-forward work performed at once. These are different levers, and none creates free capacity.
Record peak device memory and end-to-end time with each configuration. Watch host-memory pressure and transfer behavior instead of assuming a fixed system-RAM requirement; checkpoint format, dtype, pipeline design, and offload strategy all change it.
Separate denoising from VAE decoding
The VAE turns latents into viewable frames and can create its own memory peak. In Stable Video Diffusion, decode_chunk_size controls how many frames the VAE decodes together; smaller chunks reduce peak memory, while very small chunks can affect temporal consistency. Generic Diffusers VAE slicing works across batch items, whereas VAE tiling divides large frames into overlapping spatial tiles and can introduce tile-to-tile tone variation.
Check that the chosen VAE supports the optimization, then compare output quality as well as memory. Solving the decode peak does not necessarily solve the denoiser’s peak.
Include decoding, encoding, and delivery
If the workflow ingests footage or exports many variants, codec hardware matters alongside AI compute. NVIDIA documents NVENC as a dedicated encoder independent of CUDA cores, with encoding and decoding able to run alongside CUDA processing in supported workflows. Codec and feature support varies by GPU generation, driver, and SDK, so verify the exact card rather than inferring support from the brand alone.
Before renting, run one representative sample and log software versions, conditioning inputs, seed, dimensions, frame count, steps, offload settings, VAE chunking, wall time, peak VRAM, and export codec. Choose the smallest configuration that completes that test with practical headroom, then scale only when repeat runs demonstrate a real throughput need.
Sources & editorial method
Official documentation supports the technical explanations. Hardware recommendations are our workload-dependent interpretation, not a measured performance guarantee.
Hugging Face Diffusers — Stable Video Diffusion ↗Hugging Face Diffusers — reduce memory usage ↗NVIDIA Video Codec SDK 13.1 — NVENC application note ↗