Accelerate ROLLCALL A100 Pipeline

Python, CUDA, Neural Networks, Docker, Pytorch, Computer Vision, Deep Learning, Software Engineering, Performance Tuning, AI Model Development · I need an experienced PyTorch/CUDA engineer to squeeze every practical second out of our ROLLCALL Wan 2.2 I2V A14B inference pipeline running on an A100 80 GB while keeping the pictures looking exactly the same. Reducing processing time is the prime objective; any change that simply trades speed for a worse image will be rejected.

The first job is a deep profile. Please time each phase separately—model loading, T5/text encoding, VAE, diffusion, decoding, FFmpeg, and all inter-segment overhead—so I can see exactly where the pipeline stalls. From my own sampling it looks as if models may be re-opened for every 5-second chunk, so post-processing and segment overhead are the first areas I’d like you to attack.

Once the slow spots are confirmed, create a persistent warm-model worker where it makes sense, tune attention kernels and precision flags that are safe for this installation, and benchmark 20/24/30-step schedules against visual fidelity. Investigate torch.compile, FlashAttention, or similar only if the versions we use are stable today—no experimental branches in production.

All optimizations must preserve: • The existing ROLLCALL API contract • Current segment checkpoint/rec