At their annual re:Invent conference in Las Vegas, Amazon’s Web Services (AWS) exemplified this trend with a series of product and service announcements primarily focused on enhancing.
The big picture: One thing that’s become clear when it comes to Generative AI is that we’re still in the early days of the technology. Major evolutions and refinements of existing products are going to be a standard part of the tech industry news cycle for some time to come.
At their annual re:Invent conference in Las Vegas, Amazon’s Web Services (AWS) exemplified this trend with a series of product and service announcements primarily focused on enhancing their existing offerings rather than introducing completely new ones.
To be clear, there were a few genuinely new entries in the firehose of announcements that have become synonymous with AWS keynote speeches – particularly regarding foundation models. Even there, however, it could be argued that the focus was largely on rebranding or replacing existing products.
Part of the reason for this approach is that big tech companies like Amazon initially succeeded in defining and creating a high-level framework for enabling GenAI. Over time, however, it has become apparent that these tools and processes haven’t fully met the needs of many customers.
Simply put, leveraging the capabilities of GenAI was, and in many cases still is, too complex for most organizations.
With this in mind, AWS focused on addressing these gaps at this year’s re:Invent. They refined tools and bundled existing products and services to make significant strides toward simplifying the creation and deployment of GenAI technologies. These efforts were designed to accommodate companies across a wide range of technical sophistication.
Notably, they tackled this challenge across an expansive set of offerings, including custom silicon, foundation models, database enhancements, developer tools, and software platforms.
Trainium 2 Chip
Starting at the silicon level, new AWS CEO Matt Garman kicked off his keynote by highlighting the company’s substantial investments in custom chips over the last decade. He pointed to the company’s prescient decision to invest in Arm-based CPUs with its Graviton chip, sharing that their Graviton-based business is now larger than AWS’s entire compute business was when Graviton launched. He then announced the general availability of the Trainium 2 chip and EC2 compute instances optimized for AI training and inference workloads using those chips.
Taking this a step further, Garman claimed that Trainium 2 represents the first viable alternative to Nvidia GPUs – most notably at a significantly lower cost of operation. While the validity of this claim remains to be seen, initial discussions around the chip’s architecture suggest it’s a significant improvement over the first-generation Trainium.
