.@cartesia runs one of the hardest inference workloads: real-time voice. Their stack has to keep long-lived streams moving, serve millions of audio minutes a day, and hold model latency around 90ms. Together gives them the managed GPU infrastructure and low-level cluster…

Article How Cartesia Runs Real-Time Voice AI on Together AI The challenge Cartesia’s growth in real-time voice AI created four specific infrastructure requirements that many hosted platforms were not fit for. Voice workloads have tight latency budgets: Voice

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Introducing The Blind Test. Two landing pages. One built by GLM 5.2 and one by Opus 4.8. Can you tell which is which? It's very difficult to get a perfect score, just try :) Video

The next generation of inference needs purpose-built infrastructure. Together AI and 5C are deploying NVIDIA GB300 NVL72 systems with high-density compute, advanced cooling, and AI-optimized storage for large-scale inference and reasoning.