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Hyperscalers can achieve 1.5x training throughput gains on AMD GPUs by moving beyond direct CUDA porting to hardware-model co-design and agentic optimization workflows.
Distilling real GPT-4o-labeled reports, not synthetic data, enables open-weight models to match proprietary LLMs for high-value clinical entity extraction.
This engineering case study identifies practical validation steps and the locality conditions in which shared caching pays off and isolates a raw-pointer fallback that omits the dependency on the current CUDA stream.
The contribution is an experimentally validated integration repair applying an existing checkpoint-alignment principle, confined to one model revision and controlled configuration; it does not establish general determinism, task-quality equivalence, concurrent-serving gains, or capacity beyond GPU memory.