In order to generate output tokens from an input prompt, LLM inference is split into two stages: prefill and decode. Prefill runs on the input tokens, populating KV caches, before entering the decode stage that generates tokens one-by-one. While a single decode step typically runs for tens of milliseconds, prefill takes substantially longer. If run on the same devices, mixing prefill with decode degrades decode performance. In this article we explore an established solution in the form of disaggregated prefill and decode, running them on separate devices to maximize both prefill throughput and decode latencies.
Working with Creative Order has been nothing short of transformative for BRU LANE Coffee.
“Working with Creative Order has been nothing short of transformative for BRU LANE Coffee. From the outset, they demonstrated an exceptional understanding of cafe branding dynamics and the unique challenges we face in the industry. The visual identity they created for us, is truly unique and innovative and really resonates with our customers.The team’s collaborative approach and commitment to excellence were evident in every aspect of our project together. They took the time to truly grasp the essence of our café experience and translated it into a compelling brand design, which we love.
Moreover, their ability to adapt and innovate throughout the process ensured that we stayed ahead in a competitive market. Their attention to detail and passion for cafe branding was clearly evident and surpassed our expectations.
I wholeheartedly recommend Creative Order to any cafe or hospitality business looking to redefine their cafe’s brand presence and make a lasting impact. Their professionalism, creativity, and dedication make them a valuable partner in achieving brand excellence.”
Warm regards, Ondrej
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