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Intelligence should be abundant, not expensive → 🤝 Together AI & Y Combinator announce partnership to deliver the first dedicated YC GPU cluster → ⚡ On-demand B200s now available on Together GPU Clusters → 🚀 Now serving MiniMax-M3 for efficient inference → All blog posts Inference Published 7/31/2026 Autoscaling endpoints for LLM inference Choosing scaling metrics, tuning windows, and budgeting for cold starts on dedicated inference. Authors Zain Hasan, SoYoung Park, Nikitha Suryadevara, Ted Cui Table of contents 40+ Models Chosen for Production...40+ Models Chosen for Production...40+ Models Chosen for Production... Summary With Dedicated Model Inference on the Together AI platform y ou can get your deployments to autoscale on metrics the inference engine actually understands, such as in-flight requests, TTFT, GPU utilization, token throughput. You can set replica bounds, pick a metric and target, and then tune two windows that control how eagerly it scales up and how patiently it scales down. 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