Accelerate ROLLCALL A100 Pipeline

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/recovery logic

When you finish, deliver:
• A profiling report with the per-stage timings above
• An optimized branch or patch set with clear toggles for your changes
• End-to-end seconds-per-5-second-segment numbers for A100 plus an A100 vs H100/H200 cost-per-usable-second comparison (include GPU rental prices you used)
• A short readme so another engineer can reproduce your results with the same Docker image

Acceptance is based on measured wall-clock improvement and maintained Gold-quality output, not synthetic benchmarks.
This video im including took 2.5 hours to make