LLM Application Frameworks: The Plumbing Behind AI-Enabled Apps
Discover the four key frameworks that connect large language models to specific data for reliable AI applications, including Haystack, LangChain, LlamaIndex, and Semantic Kernel.
Alexis Rowe
Microsoft's Azure CTO Mark Russinovich has lifted the hood on Azure's hardware innovations, showcasing a range of solutions designed to make its data centers more efficient and powerful. The highlight of his presentation was the introduction of Azure Boost, a hardware-based tool that offloads functionality from the Azure Hypervisor, significantly improving virtual machine (VM) performance and efficiency.
The need for data center efficiency has become critical, as hyperscalers like Azure are now a significant part of the load on the network, particularly with the power requirements of large generative AI models like ChatGPT. Microsoft has made ambitious climate goals, and increasing data center efficiency is key to meeting them. Azure Boost is a crucial step in this direction, enabling Microsoft to run more virtual machines on the same hardware while reducing the environmental impact.
Azure Boost is a custom-built card that hosts networking and storage functions, as well as improved I/O capabilities. It sits outside the tenant boundaries, allowing its functions to be shared securely by everyone using the same server to host VMs. The card itself runs Azure Linux on a set of Arm cores and is built around an Intel Agilex FPGA, enabling Microsoft to develop new versions of Azure Boost and deploy them to existing servers without requiring new cards and extended downtime.
The impact of Azure Boost on VM performance is significant. It accelerates remote storage, delivering it via hardware NVMe interfaces rather than hypervisor-managed SCSI, resulting in a 15% increase in IOPS and 12% higher bandwidth. For local storage, the performance jump is even more impressive, with IOPS increasing from 3.8M to 6.6M and storage throughput from 17.2GBps to 36GBps. Additionally, Azure Boost's dual top-of-rack links enable up to 200Gbps throughput, a nine-times improvement.
Azure Boost also helps ensure that all hardware is used as much as possible, with minimum downtime for infrastructure updates. Russinovich demonstrated updating the network stack on Azure Boost, which can be done in under 250ms, with minimal network freeze and no effect on current connections. This enables Azure to quickly add capacity and instances to support serverless operations, a key aspect of its future strategy.
Microsoft is also deploying custom smart switch hardware, based on its SONiC software-defined networking (SDN) stack, to improve Azure's networking hardware. This allows the SDN appliance to manage more than 1.5 million connections per second, and performance can be increased by simply adding more DPUs to the SDN appliances.
In addition to Azure Boost, Russinovich highlighted several other innovations, including confidential computing features, such as a new integrated HSM and trusted execution environments (TEEs) for GPUs. He also showcased Azure Confidential Clean Rooms, which enable organizations to share data and functionality without exposing sensitive information.
The implications of these innovations are far-reaching, enabling Microsoft to support secure cloud workloads at scale and paving the way for a serverless future. As Russinovich noted, "We believe the future of cloud is serverless," and Azure Boost is a critical step in achieving this vision. With its focus on efficiency, performance, and security, Microsoft is well-positioned to lead the cloud computing market into a new era of innovation and growth.
Russinovich's presentation provided a fascinating glimpse into the infrastructure behind Azure's web pages, showcasing the company's commitment to innovation and its vision for the future of cloud computing. As the cloud landscape continues to evolve, Microsoft's Azure Boost and related innovations are likely to play a significant role in shaping its direction.
Discover the four key frameworks that connect large language models to specific data for reliable AI applications, including Haystack, LangChain, LlamaIndex, and Semantic Kernel.
OpenAI launches ChatGPT Pro, a premium subscription plan offering unlimited access to advanced AI models, including the enhanced o1 'reasoning' model, for a hefty $200 per month.
OpenAI's Sora video generator is now available, but a key feature that uses uploaded photos or footage of real people as references is being withheld due to safety concerns.
Copyright © 2024 Starfolk. All rights reserved.