IBM today unveiled new single frame and rack mount configurations of IBM z16 and IBM LinuxONE 4, expanding their capabilities to a broader range of data center environments. Based on IBM's Telum processor, the new options are designed with sustainability in mind for highly efficient data centers, helping clients adapt to a digitized economy and ongoing global uncertainty.

Introduced in April 2022, the IBM z16 multi frame has helped transform industries with real-time AI inferencing at scale and quantum-safe cryptography. IBM LinuxONE Emperor 4, launched in September 2022, features capabilities that can reduce both energy consumption and data center floor space while delivering the scale, performance and security that clients need. The new single frame and rack mount configurations expand client infrastructure choices and help bring these benefits to data center environments where space, sustainability and standardization are paramount.

We've created GPT-4, the latest milestone in OpenAI's effort in scaling up deep learning. GPT-4 is a large multimodal model (accepting image and text inputs, emitting text outputs) that, while less capable than humans in many real-world scenarios, exhibits human-level performance on various professional and academic benchmarks. For example, it passes a simulated bar exam with a score around the top 10% of test takers; in contrast, GPT-3.5's score was around the bottom 10%. We've spent 6 months iteratively aligning GPT-4 using lessons from our adversarial testing program as well as ChatGPT, resulting in our best-ever results (though far from perfect) on factuality, steerability, and refusing to go outside of guardrails.

Over the past two years, we rebuilt our entire deep learning stack and, together with Azure, co-designed a supercomputer from the ground up for our workload. A year ago, we trained GPT-3.5 as a first "test run" of the system. We found and fixed some bugs and improved our theoretical foundations. As a result, our GPT-4 training run was (for us at least!) unprecedentedly stable, becoming our first large model whose training performance we were able to accurately predict ahead of time. As we continue to focus on reliable scaling, we aim to hone our methodology to help us predict and prepare for future capabilities increasingly far in advance—something we view as critical for safety.