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Aggregate latency masks crucial insights into LLM inference efficiency, revealing that backend changes and quantization effects significantly influence performance metrics.
DRIFT slashes communication time from 97% to under 6% of the forward-pass time, enabling unprecedented speedups in distributed Fourier Neural Operators.
