Video generation is among the most resource-intensive AI workloads in 2026. Models like OpenAI's Sora, Google DeepMind's Veo, Runway Gen-3, and various open-source alternatives (Mochi, CogVideoX) produce short-form video clips from text prompts or image conditioning.
The workload is VRAM-intensive due to the temporal dimension: a 5-second 1080p clip at 24fps requires processing 120 frames jointly, multiplying VRAM consumption compared to single-image diffusion. State-of-the-art models typically require 24 GB minimum for inference at moderate resolution and substantially more for training.
Generation is also compute-heavy: producing a 5-second clip typically takes 1-5 minutes even on top-tier GPUs. Memory bandwidth and FP16/BF16 throughput dominate inference cost. Datacenter GPUs are strongly preferred for production deployments, with consumer cards limited to short low-resolution outputs.