3 posts tagged with "prioritization"

In the fast-evolving space of generative AI, OpenAI's models are the go-to choice for most companies for building AI-driven applications. But that may change soon as open-source models catch up by offering much better economics and data privacy through self-hosted models. One of the notable competitors in this sector is Mistral AI, a French startup, known for its innovative and lightweight models, such as the open-source Mistral 7B. Mistral has gained attention in the industry, particularly because their model is free to use and can be self-hosted. However, generative AI workloads are computationally expensive, and due to the limited supply of Graphics Processing Units (GPUs), scaling them up quickly is a complex challenge. Given the insatiable hunger for LLM APIs within organizations, there is a potential imbalance between demand and supply. One possible solution is to prioritize access to LLM APIs based on request criticality while ensuring fair access among users during peak usage. At the same time, it is important to ensure that the provisioned GPU infrastructure gets maximum utilization.

In this blog post, we will discuss how FluxNinja Aperture's Concurrency Scheduling and Request Prioritization features significantly reduce latency and ensure fairness, at no added cost, when executing generative AI workloads using the Mistral 7B Model. By improving performance and user experience, this integration is a game-changer for developers focusing on building cutting-edge AI applications.

Since CodeRabbit launched a couple of months ago, it has received an enthusiastic response and hundreds of sign-ups. CodeRabbit has been installed in over 1300 GitHub organizations and typically reviews more than 2000 pull requests per day. Furthermore, the usage continues to grow at a rapid pace, we are experiencing a healthy week-over-week growth.

While this rapid growth is encouraging, we've encountered challenges with OpenAI's stringent rate limits, particularly for the newer gpt-4 model that powers CodeRabbit. In this blog post, we will delve into the details of OpenAI rate limits and explain how we leveraged the FluxNinja's Aperture load management platform to ensure a reliable experience as we continue to grow our user base.

Imagine a bustling highway system, a complex network of roads, bridges, tunnels, and intersections, each designed to handle a certain amount of traffic. Now, consider the events that lead to traffic jams - accidents, road work, or a sudden influx of vehicles. These incidents cause traffic to back up, and often, a jam in one part of the highway triggers a jam in another. A bottleneck on a bridge, for example, can lead to a jam on the road leading up to it. Congestion creates many complications, from delays and increased travel times, to drivers getting annoyed over wasted time and too much fuel burned. These disruptions don’t just hurt the drivers, they hit the whole economy. Goods are delayed and services are disrupted as employees arrive late (and angry) at work.

But highway systems are not left to the mercy of these incidents. Over the years, they have evolved to incorporate a multitude of strategies to handle such failures and unexpected events. Emergency lanes, traffic lights, and highway police are all part of the larger traffic management system. When congestion occurs, traffic may be re-routed to alternate routes. During peak hours, on-ramps are metered to control the influx of vehicles. If an accident occurs, the affected lanes are closed, and traffic is diverted to other lanes. Despite their complexities and occasional hiccups, these strategies aim to manage traffic as effectively as possible.