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AWS–Reactor: hardware-tuned streaming video inference on Trainium

Contribution grade

Streaming video inference systems · Human-designed research method or resource

Reference score40,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Optimized a Rolling Forcing streaming pipeline with NKI kernels for3D rotary embeddings and cache copies, fused attention transposes, hybrid head/sequence sharding and batching of common denoise/cache-update work.

Scientific scope

Optimized a Rolling Forcing streaming pipeline with NKI kernels for3D rotary embeddings and cache copies, fused attention transposes, hybrid head/sequence sharding and batching of common denoise/cache-update work. Company technical account supports the integration, not a new Rolling Forcing algorithm, invention of NKI/parallelism or independently certified real-time performance. Kernel latency and full-pipeline frame rate are different metrics; reported single-chip results depend on model, resolution and configuration. Article writer Stephen Zorio is not asserted to be a complete technical-inventor list; corporate project groups retained.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Company technical account supports the integration, not a new Rolling Forcing algorithm, invention of NKI/parallelism or independently certified real-time performance. Kernel latency and full-pipeline frame rate are different metrics; reported single-chip results depend on model, resolution and configuration. Article writer Stephen Zorio is not asserted to be a complete technical-inventor list; corporate project groups retained.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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