With the rise of physical AI, video content generation has surged exponentially. A single camera-equipped autonomous vehicle can generate more than 1 TB of video daily, while a robotics-powered manufacturing facility may produce 1 PB of data daily. To leverage this data for training and fine-tuning world foundation models (WFMs), you must first process it efficiently.
]]>Deploying large language models (LLMs) in production environments often requires making hard trade-offs between enhancing user interactivity and increasing system throughput. While enhancing user interactivity requires minimizing time to first token (TTFT), increasing throughput requires increasing tokens per second. Improving one aspect often results in the decline of the other…
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