Running Sora (or Sora 2) at scale demands addressing substantial compute, memory, throughput, and infrastructure complexity. First, video generation is far more expensive than static image generation: multiple frames, temporal context, and cross-frame attention multiply the computational load. Keeping latency low enough for user responsiveness or preview experiences is nontrivial.
Second, model partitioning and memory optimization are key. A transformer over spatio-temporal patches must store and compute attention across many tokens; GPU memory may be a bottleneck, especially for higher resolutions or longer durations. Techniques like sparse attention, patch subsampling, or attention pruning may be necessary. Also, the latent representations must be compressed and decoded efficiently into video frames without causing bottlenecks in I/O, decoding, and streaming.
Third, handling throughput and concurrency is a challenge. As many users generate videos concurrently, scheduling, GPU resource allocation, queuing, batching, and load balancing become critical. The system must also gracefully handle failures, fallback logic, and regeneration under load. Finally, storing, retrieving, and indexing generated video assets (and embedding metadata) requires scalable storage, caching, and fast retrieval infrastructure. Ensuring the entire pipeline — prompt processing, generation, decoding, moderation, embedding indexing — runs reliably and cost-effectively is a complex engineering challenge.