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· 11 min read
Karanbir Sohi

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.