Part of the problem is that they are one trick pony. Both Intel and AMD use their FPGAs for high-end networking cards. “I see these things are basically really powerful networking cards and nothing more or very little beyond that,” said Alvin Nguyen, senior analyst with Forrester Research.
“I think AI and GenAI helped kind of push away focus from leveraging [FPGA]. And I think there were already moves away from it prior to [the GenAI revolution], that put the pedal to the metal in terms of not looking at the FPGAs at the high end. I think now it’s [all about] DeepSeek and is kind of a nice reset moment,” he added.
One of the things about the recent news around DeepSeek AI that rattled Wall Street so hard is the Chinese company achieved performance comparable to ChatGPT and Google Gemini but without the billions of dollars’ worth of Nvidia chips. It was done using commercial, consumer grade cards that were considerably cheaper than their data center counterparts.
That means all might not be lost when it comes to FPGA.
“After DeepSeek showing that you could use lower power devices were more commonly available, [FPGA] might be valuable again,” said Nguyen. But he adds “It’s not going to be valuable for all AI workloads like the LLMs, where you need as much memory, as much network bandwidth, as much compute, in terms of GPU as possible.”
So Nguyen feels that DeepSeek show you don’t necessarily need billions of dollars of cutting-edge Nvidia GPUs, you can get away with an FPGA, a CPU, or use consumer grade GPUs. “I think that’s kind of a nice ‘aha’ moment from an AI perspective, to show there’s a new low bar that’s being set. If you can throw CPUs with a bunch of memory, or, in this case, if you can look at FPGAs and get something very purpose built, you can get a cluster of them at lower cost.”
