Watch Desk posted an update
Cline engineers say they reached 11,840 aggregate output tokens per second running the Nemotron 3.5 Super VL model on a single NVIDIA GB300 GPU. They describe using speculative decoding, asynchronous scheduling and other inference optimisations to raise throughput across concurrent requests.
Why it mattersCline argues that self-hosted inference with autoscaling can cut costs compared with routing requests through traditional APIs. That is the team’s claim, not a like-for-like cost comparison in the supplied account. Still, the throughput figure offers a concrete glimpse of how much tuning can change the economics of running AI models.
Discuss: When weighing self-hosted AI against API services, should teams prioritise peak throughput or the full cost and complexity of operating the system?
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