Enlarge / Scale is important, especially when talking about relative console sales. (credit: Aurich Lawson | Getty Images)

Yesterday, Microsoft announced that it made 31 percent less off Xbox hardware in the first quarter of 2024 (ending in March) than it had the year before, a decrease it says was "driven by lower volume of consoles sold." And that's not because the console sold particularly well a year ago, either; Xbox hardware revenue for the first calendar quarter of 2023 was already down 30 percent from the previous year.

Those two data points speak to a console that is struggling to substantially increase its player base during a period that should, historically, be its strongest sales period. But getting wider context on those numbers is a bit difficult because of how Microsoft reports its Xbox sales numbers (i.e., only in terms of quarterly changes in total console hardware revenue). Comparing those annual shifts to the unit sales numbers that Nintendo and Sony report every quarter is not exactly simple.

Context clues

Significant declines in Xbox hardware revenue for four of the last five quarters stand out relative to competitors' unit sales. (credit: Kyle Orland)

To attempt some direct contextual comparison, we took unit sales numbers for some recent successful Sony and Nintendo consoles and converted them to Microsoft-style year-over-year percentage changes (aligned with the launch date for each console). For this analysis, we skipped over each console's launch quarter, which contains less than three months of total sales (and often includes a lot of pent-up early adopter demand). We also skipped the first four quarters of a console's life cycle, which don't have a year-over-year comparison point from 12 months prior.

Enlarge / Tech brands love hollering about the purported thrills of AI these days. (credit: Getty )

Logitech announced a new mouse last week. A company rep reached out to inform Ars of Logitech’s “newest wireless mouse.” The gadget’s product page reads the same as of this writing.

I’ve had good experience with Logitech mice, especially wireless ones, one of which I'm using now . So I was keen to learn what Logitech might have done to improve on its previous wireless mouse designs. A quieter click ? A new shape to better accommodate my overworked right hand? Multiple onboard profiles in a business-ready design?

I was disappointed to learn that the most distinct feature of the Logitech Signature AI Edition M750 is a button located south of the scroll wheel. This button is preprogrammed to launch the ChatGPT prompt builder, which Logitech recently added to its peripherals configuration app Options+.

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In the world of AI, what might be called "small language models" have been growing in popularity recently because they can be run on a local device instead of requiring data center-grade computers in the cloud. On Wednesday, Apple introduced a set of tiny source-available AI language models called OpenELM that are small enough to run directly on a smartphone. They're mostly proof-of-concept research models for now, but they could form the basis of future on-device AI offerings from Apple.

Apple's new AI models, collectively named OpenELM for "Open-source Efficient Language Models," are currently available on the Hugging Face under an Apple Sample Code License . Since there are some restrictions in the license, it may not fit the commonly accepted definition of "open source," but the source code for OpenELM is available.

On Tuesday, we covered Microsoft's Phi-3 models , which aim to achieve something similar: a useful level of language understanding and processing performance in small AI models that can run locally. Phi-3-mini features 3.8 billion parameters, but some of Apple's OpenELM models are much smaller, ranging from 270 million to 3 billion parameters in eight distinct models.

Enlarge (credit: Qualcomm)

Qualcomm’s Snapdragon X series of chips promises to be the company’s first that can go toe-to-toe with Apple Silicon, and the PC ecosystem is reacting accordingly . Microsoft reportedly plans for the Arm version of its next Surface tablet to be the flagship, and major apps like Chrome and Dropbox have recently released Arm-native Windows versions for the first time.

Ahead of the chips' launch late this year, Qualcomm announced a new lower-end model destined for cheaper devices. Dubbed the Snapdragon X Plus, it shares a lot in common with the flagship Snapdragon X Elite .

The Snapdragon X Plus includes 10 CPU cores instead of the Elite’s 12, though the more noticeable change is its lack of support for clock-speed boosting; the chip’s 3.4 GHz base frequency is as fast as it goes, where the Elite chips can boost two cores to 4.2 GHz and one core up to 4.3 GHz, depending on the specific model. Qualcomm also rates the X Plus’ integrated GPU at 3.8 TFLOPs, down from the X Elite’s maximum of 4.6 TFLOPs. Aside from those high-level FLOP numbers, we still know very little about how the GPU will be configured; we also don’t know the ratio of “big” and “little” CPU cores.

Enlarge (credit: Getty Images)

On Tuesday, Microsoft announced a new, freely available lightweight AI language model named Phi-3-mini, which is simpler and less expensive to operate than traditional large language models (LLMs) like OpenAI's GPT-4 Turbo . Its small size is ideal for running locally, which could bring an AI model of similar capability to the free version of ChatGPT to a smartphone without needing an Internet connection to run it.

The AI field typically measures AI language model size by parameter count. Parameters are numerical values in a neural network that determine how the language model processes and generates text. They are learned during training on large datasets and essentially encode the model's knowledge into quantified form. More parameters generally allow the model to capture more nuanced and complex language-generation capabilities but also require more computational resources to train and run.

Some of the largest language models today, like Google's PaLM 2 , have hundreds of billions of parameters. OpenAI's GPT-4 is rumored to have over a trillion parameters but spread over eight 220-billion parameter models in a mixture-of-experts configuration. Both models require heavy-duty data center GPUs (and supporting systems) to run properly.