Today, some of the most prevalent AI-powered applications are highly interactive ones: coding companions, customer service chatbots, and others. While they all have different measures of success, they’re increasingly aligned on one… Read More
In the early days of transformers-based AI workloads, generation speed wasn’t that big of an issue. Users generally tolerated slower inference due to novelty, and serious use cases outside of… Read More
Time-to-first-token, or TTFT, has generally been one of many key performance indicators for AI-based applications. Consumer-facing chatbots, for example, might need it to be very low, while enterprise cases can… Read More
Large-scale AI workloads are deployed by stringing together many cards and many nodes. Any increase in model size, context length or users impacts the number of cards needed. Gone are… Read More
We’ve become accustomed to a brand new model coming out every few weeks that one-ups the last one at this point. Developers don’t sit with just a single model—in fact,… Read More
The future of AI workflows is almost certainly going to be multi-modal agents. But rather than incredibly complex, compute-hungry multimodal models, there’s already a much easier pathway to get there. … Read More
‘AI Inference’ has been trending everywhere, from keynote speeches to quarterly earnings reports and in the news. You have probably heard phrases like “inference-time compute”, “reasoning” and “AI deployment” and… Read More
