The computing power required for large-model video processing depends on several factors, including the resolution and length of the video, the complexity of the model, the frame rate, and the specific tasks being performed (e.g., video generation, analysis, enhancement, or compression). Generally, video processing with large models demands substantial computational resources due to the high volume of data involved and the need for real-time or near-real-time inference.
For example, processing a 4K video at 60 frames per second using a large generative model like those used in video synthesis or deep learning-based video enhancement can require hundreds of teraflops (TFLOPS) of compute performance. If the model is transformer-based, such as those used in vision-language tasks combined with video, the demand can be even higher due to the attention mechanisms over spatiotemporal data.
A single high-resolution video frame (e.g., 3840x2160 pixels) processed through a large neural network can take several gigabytes of memory and significant GPU time. When processing video sequentially, the cumulative compute load increases linearly with the number of frames. For instance, a 1-minute 4K video has about 3,600 frames. Processing each frame through a large diffusion or transformer model could consume anywhere from several to tens of gigaflops per frame, leading to an aggregate demand in the range of tens to hundreds of petaflops for just one minute of video, depending on parallelization.
To handle such workloads efficiently, distributed computing systems with multiple GPUs or TPUs are typically employed. Accelerated computing hardware such as NVIDIA A100, H100 GPUs, or specialized AI accelerators are commonly used. Cloud-based infrastructure is often leveraged to scale resources dynamically based on workload.
In the context of cloud services, platforms like Tencent Cloud offer a range of GPU-accelerated computing solutions suitable for large-model video processing. For instance, Tencent Cloud's GPU instances, such as those powered by NVIDIA A100 or V100, provide the high-performance compute capability needed for training and inference of large video models. Additionally, Tencent Cloud's Elastic GPU Service and managed AI platforms allow flexible deployment and scaling of resource-intensive video processing tasks. These services support containerized environments and integrate well with machine learning workflows, enabling efficient handling of large-scale video data.