DeepSeek tests “sparse attention” to slash AI processing costs
SMRTR summary
Chinese AI company DeepSeek released an experimental language model using "sparse attention" technology that cuts processing costs by 50 percent, addressing the computational bottleneck that slows AI during long conversations. Instead of analyzing every word relationship like traditional models, DeepSeek's approach selectively examines only the most relevant connections, potentially making AI inference dramatically more affordable.
SMRTR provides this summary for quick context. The original article belongs to Ars Technica.
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