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Nvidia's Vera Rubin platform combines CPUs and GPUs into a single system, reflecting the company's growing ambition to power every layer of AI infrastructure. Nvidia is hyping up its new Vera Rubin chip system this week, revealing new performance benchmarks for the GPU and CPU combo ahead of rival AMD's annual product event in San Francisco on Thursday. During a lengthy technical workshop last week at the company's headquarters in Santa Clara, California, Nvidia executives boasted to a small group of journalists about the chip system's increased power and efficiency capabilities. The biggest takeaway: Nvidia, which has long specialized in making GPUs, is increasingly trying to position itself as a supplier of CPUs that can power AI agents. While GPUs are still the main hardware that companies use to train and run their AI models, the industry's shift toward more complex, agentic systems has increased demand for CPUs, which can orchestrate data flows, networking, and other software tasks.

PU (Positive Unlabeled) learning is a variant of supervised classification learning in which the only labels revealed to the learner are of positively labeled instances. PU learning arises in many real-world applications. Most existing work relies on the simplifying assumptions that the positively labeled training data is drawn from the restriction of the data generating distribution to positively labeled instances and/or that the proportion of positively labeled points (a.k.a. the class prior) is known apriori to the learner. This paper provides a theoretical analysis of the statistical complexity of PU learning under a wider range of setups. Unlike most prior work, our study does not assume that the class prior is known to the learner. We prove upper and lower bounds on the required sample sizes (of both the positively labeled and the unlabeled samples).

Large Language Model (LLM) inference on Central Processing Units (CPU) is challenging due to the vast quantities of Multiply-Add (MAD) matrix operations in the attention computations.

The days of tech giants buying up discrete chips are over. AI companies now need GPUs, CPUs, and everything in between. Ask anyone what Nvidia makes, and they're likely to first say "GPUs." For decades, the chipmaker has been defined by advanced parallel computing, and the emergence of generative AI and the resulting surge in demand for GPUs has been a boon for the company . But Nvidia's recent moves signal that it's looking to lock in more customers at the less compute-intensive end of the AI market--customers who don't necessarily need the beefiest, most powerful GPUs to train AI models, but instead are looking for the most efficient ways to run agentic AI software.

IMPALA: scalable distributed deep-rl with importance weighted actor-learner architectures.69

Ethical questions are thus not sufficiently prominent in this work to warrant a dedicated discussion section. In general, we believe, this work will have an overall positive impact asitcan help shed light into theblack-box that isdeep learning.