AMD targets AI PCs to curb agentic AI costs as enterprises rethink cloud token economics
As AI moves beyond chatbots toward autonomous agents, attention is shifting enterprise AI PCs as a new layer of AI infrastructure. That transition is driving demand for hardware and software designed to run AI workloads locally.
Simple chatbots provided incremental productivity gains while agentic AI utilizes fully reasoning autonomous agents. This transition transforms modest efficiency improvements into massive productivity multipliers, effectively enabling agents to work autonomously on behalf of users, according to Rahul Tikoo (pictured), senior vice president and general manager of the Client Business Unit at Advanced Micro Devices Inc.
“Now you can imagine a software developer that has an army of agents working for them and creating code,” Tikoo said. “That’s the world we’re entering into and PCs are becoming a very important part of that world.”
Tikoo spoke with theCUBE’s John Furrier and Dave Vellante at the AMD Advancing AI event, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed the shift toward agentic AI running on personal computers and the evolving role of enterprise AI PCs. ( Disclosure below.)*
Enterprise AI PCs tackle token costs
In today’s environment, one of the biggest concerns raised by CIOs is the cost of agentic AI, particularly from a cloud infrastructure perspective, according to Tikoo. Many are realizing those expenses can become a significant line item on the profit-and-loss statement.
“From that perspective, tokenomics has really become an important consideration for C-suites on how to deploy agentic,” Tikoo said. “And so when you think about agentic PCs and the stuff that we’re working on, that’s become an important strategic node for these CIOs in a hybrid AI infrastructure.”
Organizations will continue to rely on both cloud and private infrastructure, but they’re increasingly viewing AI PCs as an extension of those environments to reduce token costs while maintaining security and low latency. To support that shift, AMD launched its Ryzen AI Halo platform.
“Now if you look at large language models, things that used to run in the frontier now can run on the endpoints,” Tikoo said. “Nine billion parameter models or 24 billion parameter models are becoming as good a quality as some of these frontier models.”
Ryzen AI Halo, with up to 128GB of memory, enables organizations to run AI models and agentic workflows locally on a single system. The approach gives developers a local sandbox to experiment with AI applications without incurring cloud token costs.
“You could start your work on the endpoint or you could deploy your work at the endpoint,” Tikoo said. “When you think about deploying the work at the endpoint, endpoint is probably going to be the best place in terms of inferencing because the data resides at the endpoint.”
CIOs increasingly understand the concepts behind agentic AI and token economics, but many are still struggling with the cost implications.
“If you think about AMD as an end-to-end silicon provider going from cloud to data center, to the endpoint, to the edge, we provide that entire scale,” Tikoo said. “Now what we’re trying to work on is making the software pieces that stitch it all together much simpler.”
Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of the AMD Advancing AI event:
( Disclosure: TheCUBE is a paid media partner for the AMD Advancing AI event. Neither AMD, the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)*
Photo: SiliconANGLE
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