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How attention offloading reduces the costs of LLM inference at scale
Attention offloading distributes LLM inference operations between high-end accelerators and consumer-grade GPUs to reduce costs.
Join us as we return to NYC on June 5th to engage with top executive leaders, delving into strategies for auditing AI models to ensure fairness, optimal performance, and ethical compliance across diverse organizations. “Adopting this heterogeneous architecture allows us to design a serving system that flexibly delivers the three essential components (i.e., computational power, memory capacity and bandwidth) for high-performance LLM inference in a cost-efficient manner,” the researchers write. “Our findings reveal that not only conventional system buses such as PCIe 4.0 could meet our needs, networking technologies like 200Gb Infiniband or even Ethernet, already widely deployed in current AI-oriented data centers nowadays, also suffice,” the researchers write.
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