Intel Inference: A CUDA Killer? Some Have Hope
December 15, 2023
This essay is the work of a dumb dinobaby. No smart software required.
Intel is embracing “inference” approach. Why? Maybe it will irritate fewer legal eagles? Maybe it is a marketing differentiator? Maybe Intel knows how to make probability less of a “problem”?
Brilliant, right? The answer to these questions are supposed to be explained in “Intel CEO Attacks Nvidia on AI: The Entire Industry Is Motivated to Eliminate the CUDA Market.” The Tom’s Hardware report uses the “attack” angle as a hook. Intel is thinking differently. The company has not had the buzz of nVidia or OpenAI. Plus, no horse metaphors.
Marketing professionals explain to engineers what must be designed, tested, and delivered in 2024. The engineers are skeptical. The marketing team is confident that their TikTok strategy will be a winner. Thanks, MSFT Copilot. Good enough.
What’s an inference? According to Bing (the stupid version), inference is “a conclusion reached on the basis of evidence and reasoning.” But in mathematics, inference has a slightly different denotation; to wit, this explanation from Britannica:
Inference, in statistics, the process of drawing conclusions about a parameter one is seeking to measure or estimate. Often scientists have many measurements of an object—say, the mass of an electron—and wish to choose the best measure. One principal approach of statistical inference is Bayesian estimation, which incorporates reasonable expectations or prior judgments (perhaps based on previous studies), as well as new observations or experimental results. Another method is the likelihood approach, in which “prior probabilities” are eschewed in favor of calculating a value of the parameter that would be most “likely” to produce the observed distribution of experimental outcomes. In parametric inference, a particular mathematical form of the distribution function is assumed. Nonparametric inference avoids this assumption and is used to estimate parameter values of an unknown distribution having an unknown functional form.
Now what does Tom’s Hardware present at Intel’s vision for its “to be” chips. I have put several segments together for the purposes of my blog post:
"You know, the entire industry is motivated to eliminate the CUDA market. [Gelsinger, the Intel CEO] said. He cited examples such as MLIR, Google, and OpenAI, suggesting that they are moving to a "Pythonic programming layer" to make AI training more open. "We think of the CUDA moat as shallow and small," Gelsinger went on. "Because the industry is motivated to bring a broader set of technologies for broad training, innovation, data science, et cetera." But Intel isn’t relying just on training. Instead, it thinks inference is the way to go. "As inferencing occurs, hey, once you’ve trained the model… There is no CUDA dependency," Gelsinger continued. "It’s all about, can you run that model well?"
CUDA is definitely the target. “CUDA” refers to nVidia’s parallel computing platform and programming model … With more than 20 million downloads to date, CUDA helps developers speed up their applications by harnessing the power of GPU accelerators.
Tom’s Hardware raises a question:
It’s a bold strategy, and Gelsinger appeared confident as he led his team through presentations today. Can he truly take on CUDA? Only time will tell as applications for the chips Intel launched today — and that his competitors are also working on — become more widespread.
Of course. With content marketing, PR professionals, and a definite need to generate some buzz in an OpenAI-dominated world, Intel will be capturing some attention. The hard part will be making sufficiently robust sales to show that an ageing company still can compete.
Stephen E Arnold, December 15, 2023
Comments
-
- Subscribe to Beyond Search
Feature archive
News archive
-
