Too many disconnected tools slow teams down and increase risk. In 2026, simplifying tech stacks and automating workflows is becoming a key advantage for scalable IT operations.
Too many disconnected tools slow teams down and increase risk. In 2026, simplifying tech stacks and automating workflows is becoming a key advantage for scalable IT operations.
Phát hiện lãng phí 12.000 USD/tháng tại công ty SaaS 40 nhân viên: nhân viên lặp lại hàng trăm lần các yêu cầu dài với AI. Kiểm tra thủ công ngắn gọn đã vạch ra lỗ hổng chi phí. Cần tối ưu chi phí AI? Hãy kiểm tra log sử dụng. #SaaS #AI #ChiPhi #ToiUuHoa #AIWaste #DigitalEfficiency
https://www.reddit.com/r/SaaS/comments/1qqewj3/we_discovered_12000_in_monthly_wastage_at_a_saas/
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Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance across diverse applications. However, their computational overhead during deployment remains a critical bottleneck. While Key-Value (KV) caching effectively trades memory for computation to enhance inference efficiency, the growing memory footprint from extensive KV caches significantly reduces throughput and restricts prolonged deployment on memory-constrained GPU devices. To address this challenge, we propose CalibQuant, a simple yet highly effective visual quantization strategy that drastically reduces both memory and computational overhead. Specifically, CalibQuant introduces an extreme 1-bit quantization scheme, complemented by novel post-scaling and calibration techniques tailored to the intrinsic patterns of KV caches, thereby ensuring high efficiency without compromising model performance. Leveraging Triton for runtime optimization, we achieve a 10x throughput increase on InternVL models. Our method is designed to be plug-and-play, seamlessly integrating with various existing MLLMs without requiring architectural changes. Extensive experiments confirm that our approach significantly reduces memory usage while maintaining computational efficiency and preserving multimodal capabilities. Codes are available at https://github.com/insuhan/calibquant.
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