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University of California
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Sparse, quantized linear-attention models can outperform dense counterparts on neuromorphic hardware, achieving up to 37脳 higher throughput and 16脳 lower power consumption.
VisualClaw slashes API costs by 98% while boosting accuracy, transforming how VLMs can operate in real-time environments.
