LLMs – Page 3

Nvidia unveils Blackwell B200, the “world’s most powerful chip” designed for AI

AI, AI inference, Biz & IT, chatgpt, chatgtp, GPU, GPUs, Jensen Huang, large language models, LLMs, machine learning, NVIDIA, openai, supercomputers / Shannon Garcia / March 19, 2024

There’s no knowing where we’re rowing —

208B transistor chip can reportedly reduce AI cost and energy consumption by up to 25x.

Benj Edwards – Mar 19, 2024 3: 27 pm UTC

Enlarge / The GB200 “superchip” covered with a fanciful blue explosion.

Nvidia / Benj Edwards

On Monday, Nvidia unveiled the Blackwell B200 tensor core chip—the company’s most powerful single-chip GPU, with 208 billion transistors—which Nvidia claims can reduce AI inference operating costs (such as running ChatGPT) and energy consumption by up to 25 times compared to the H100. The company also unveiled the GB200, a “superchip” that combines two B200 chips and a Grace CPU for even more performance.

The news came as part of Nvidia’s annual GTC conference, which is taking place this week at the San Jose Convention Center. Nvidia CEO Jensen Huang delivered the keynote Monday afternoon. “We need bigger GPUs,” Huang said during his keynote. The Blackwell platform will allow the training of trillion-parameter AI models that will make today’s generative AI models look rudimentary in comparison, he said. For reference, OpenAI’s GPT-3, launched in 2020, included 175 billion parameters. Parameter count is a rough indicator of AI model complexity.

Nvidia named the Blackwell architecture after David Harold Blackwell, a mathematician who specialized in game theory and statistics and was the first Black scholar inducted into the National Academy of Sciences. The platform introduces six technologies for accelerated computing, including a second-generation Transformer Engine, fifth-generation NVLink, RAS Engine, secure AI capabilities, and a decompression engine for accelerated database queries.

Enlarge / Press photo of the Grace Blackwell GB200 chip, which combines two B200 GPUs with a Grace CPU into one chip.

Several major organizations, such as Amazon Web Services, Dell Technologies, Google, Meta, Microsoft, OpenAI, Oracle, Tesla, and xAI, are expected to adopt the Blackwell platform, and Nvidia’s press release is replete with canned quotes from tech CEOs (key Nvidia customers) like Mark Zuckerberg and Sam Altman praising the platform.

GPUs, once only designed for gaming acceleration, are especially well suited for AI tasks because their massively parallel architecture accelerates the immense number of matrix multiplication tasks necessary to run today’s neural networks. With the dawn of new deep learning architectures in the 2010s, Nvidia found itself in an ideal position to capitalize on the AI revolution and began designing specialized GPUs just for the task of accelerating AI models.

Nvidia’s data center focus has made the company wildly rich and valuable, and these new chips continue the trend. Nvidia’s gaming GPU revenue ($2.9 billion in the last quarter) is dwarfed in comparison to data center revenue (at $18.4 billion), and that shows no signs of stopping.

A beast within a beast

Enlarge / Press photo of the Nvidia GB200 NVL72 data center computer system.

The aforementioned Grace Blackwell GB200 chip arrives as a key part of the new NVIDIA GB200 NVL72, a multi-node, liquid-cooled data center computer system designed specifically for AI training and inference tasks. It combines 36 GB200s (that’s 72 B200 GPUs and 36 Grace CPUs total), interconnected by fifth-generation NVLink, which links chips together to multiply performance.

Enlarge / A specification chart for the Nvidia GB200 NVL72 system.

“The GB200 NVL72 provides up to a 30x performance increase compared to the same number of NVIDIA H100 Tensor Core GPUs for LLM inference workloads and reduces cost and energy consumption by up to 25x,” Nvidia said.

That kind of speed-up could potentially save money and time while running today’s AI models, but it will also allow for more complex AI models to be built. Generative AI models—like the kind that power Google Gemini and AI image generators—are famously computationally hungry. Shortages of compute power have widely been cited as holding back progress and research in the AI field, and the search for more compute has led to figures like OpenAI CEO Sam Altman trying to broker deals to create new chip foundries.

While Nvidia’s claims about the Blackwell platform’s capabilities are significant, it’s worth noting that its real-world performance and adoption of the technology remain to be seen as organizations begin to implement and utilize the platform themselves. Competitors like Intel and AMD are also looking to grab a piece of Nvidia’s AI pie.

Nvidia says that Blackwell-based products will be available from various partners starting later this year.

Nvidia unveils Blackwell B200, the “world’s most powerful chip” designed for AI Read More »

Nvidia sued over AI training data as copyright clashes continue

AI models, chatbots, copyright, copyright infringement, copyright law, large language models, LLMs, NVIDIA, Policy / Kelly Newman / March 11, 2024

In authors’ bad books —

Copyright suits over AI training data reportedly decreasing AI transparency.

Ashley Belanger – Mar 11, 2024 4: 35 pm UTC

Book authors are suing Nvidia, alleging that the chipmaker’s AI platform NeMo—used to power customized chatbots—was trained on a controversial dataset that illegally copied and distributed their books without their consent.

In a proposed class action, novelists Abdi Nazemian (Like a Love Story), Brian Keene (Ghost Walk), and Stewart O’Nan (Last Night at the Lobster) argued that Nvidia should pay damages and destroy all copies of the Books3 dataset used to power NeMo large language models (LLMs).

The Books3 dataset, novelists argued, copied “all of Bibliotek,” a shadow library of approximately 196,640 pirated books. Initially shared through the AI community Hugging Face, the Books3 dataset today “is defunct and no longer accessible due to reported copyright infringement,” the Hugging Face website says.

According to the authors, Hugging Face removed the dataset last October, but not before AI companies like Nvidia grabbed it and “made multiple copies.” By training NeMo models on this dataset, the authors alleged that Nvidia “violated their exclusive rights under the Copyright Act.” The authors argued that the US district court in San Francisco must intervene and stop Nvidia because the company “has continued to make copies of the Infringed Works for training other models.”

A Hugging Face spokesperson clarified to Ars that “Hugging Face never removed this dataset, and we did not host the Books3 dataset on the Hub.” Instead, “Hugging Face hosted a script that downloads the data from The Eye, which is the place where ELeuther hosted the data,” until “Eleuther removed the data from The Eye” over copyright concerns, causing the dataset script on Hugging Face to break.

Nvidia did not immediately respond to Ars’ request to comment.

Demanding a jury trial, authors are hoping the court will rule that Nvidia has no possible defense for both allegedly violating copyrights and intending “to cause further infringement” by distributing NeMo models “as a base from which to build further models.”

AI models decreasing transparency amid suits

The class action was filed by the same legal team representing authors suing OpenAI, whose lawsuit recently saw many claims dismissed, but crucially not their claim of direct copyright infringement. Lawyers told Ars last month that authors would be amending their complaints against OpenAI and were “eager to move forward and litigate” their direct copyright infringement claim.

In that lawsuit, the authors alleged copyright infringement both when OpenAI trained LLMs and when chatbots referenced books in outputs. But authors seemed more concerned about alleged damages from chatbot outputs, warning that AI tools had an “uncanny ability to generate text similar to that found in copyrighted textual materials, including thousands of books.”

Uniquely, in the Nvidia suit, authors are focused exclusively on Nvidia’s training data, seemingly concerned that Nvidia could empower businesses to create any number of AI models on the controversial dataset, which could affect thousands of authors whose works could allegedly be broadly infringed just by training these models.

There’s no telling yet how courts will rule on the direct copyright claims in either lawsuit—or in the New York Times’ lawsuit against OpenAI—but so far, OpenAI has failed to convince courts to toss claims aside.

However, OpenAI doesn’t appear very shaken by the lawsuits. In February, OpenAI said that it expected to beat book authors’ direct copyright infringement claim at a “later stage” of the case and, most recently in the New York Times case, tried to convince the court that NYT “hacked” ChatGPT to “set up” the lawsuit.

And Microsoft, a co-defendant in the NYT lawsuit, even more recently introduced a new argument that could help tech companies defeat copyright suits over LLMs. Last month, Microsoft argued that The New York Times was attempting to stop a “groundbreaking new technology” and would fail, just like movie producers attempting to kill off the VCR in the 1980s.

“Despite The Times’s contentions, copyright law is no more an obstacle to the LLM than it was to the VCR (or the player piano, copy machine, personal computer, Internet, or search engine),” Microsoft wrote.

In December, Hugging Face’s machine learning and society lead, Yacine Jernite, noted that developers appeared to be growing less transparent about training data after copyright lawsuits raised red flags about companies using the Books3 dataset, “especially for commercial models.”

Meta, for example, “limited the amount of information [it] disclosed about” its LLM, Llama-2, “to a single paragraph description and one additional page of safety and bias analysis—after [its] use of the Books3 dataset when training the first Llama model was brought up in a copyright lawsuit,” Jernite wrote.

Jernite warned that AI models lacking transparency could hinder “the ability of regulatory safeguards to remain relevant as training methods evolve, of individuals to ensure that their rights are respected, and of open science and development to play their role in enabling democratic governance of new technologies.” To support “more accountability,” Jernite recommended “minimum meaningful public transparency standards to support effective AI regulation,” as well as companies providing options for anyone to opt out of their data being included in training data.

“More data transparency supports better governance and fosters technology development that more reliably respects peoples’ rights,” Jernite wrote.

Nvidia sued over AI training data as copyright clashes continue Read More »

Reddit cashes in on AI gold rush with $203M in LLM training license fees

AI, Data, Google, legal, license, LLMs, reddit, training / DJ Henderson / February 25, 2024

Your posts are the product —

Two- to three-year deals with Google, others, come amid legal uncertainty over “fair use.”

Kyle Orland – Feb 23, 2024 5: 13 pm UTC

Enlarge / “Reddit Gold” takes on a whole new meaning when AI training data is involved.

The last week saw word leak that Google had agreed to license Reddit’s massive corpus of billions of posts and comments to help train its large language models. Now, in a recent Securities and Exchange Commission filing, the popular online forum has revealed that it will bring in $203 million from that and other unspecified AI data licensing contracts over the next three years.

Reddit’s Form S-1—published by the SEC late Thursday ahead of the site’s planned stock IPO—says the company expects $66.4 million of that data-derived value from LLM companies to come during the 2024 calendar year. Bloomberg previously reported the Google deal to be worth an estimated $60 million a year, suggesting that the three-year deal represents the vast majority of its AI licensing revenue so far.

Google and other AI companies that license Reddit’s data will receive “continuous access to [Reddit’s] data API as well as quarterly transfers of Reddit data over the term of the arrangement,” according to the filing. That constant, real-time access is particularly valuable, the site writes in the filing, because “Reddit data constantly grows and regenerates as users come and interact with their communities and each other.”

“Why pay for the cow…?”

While Reddit sees data licensing to AI firms as an important part of its financial future, its filing also notes that free use of its data has already been “a foundational part of how many of the leading large language models have been trained.” The filing seems almost bitter in noting that “some companies have constructed very large commercial language models using Reddit data without entering into a license agreement with us.”

That acknowledgment highlights the still-murky legal landscape over AI companies’ penchant for scraping huge swathes of the public web for training purposes, a practice those companies defend as fair use. And Reddit seems well aware that AI models may continue to hoover up its posts and comments for free, even as it tries to sell that data to others.

“Some companies may decline to license Reddit data and use such data without license given its open nature, even if in violation of the legal terms governing our services,” the company writes. “While we plan to vigorously enforce against such entities, such enforcement activities could take years to resolve, result in substantial expense, and divert management’s attention and other resources, and we may not ultimately be successful.”

Yet the mere existence of AI data licensing agreements like Reddit’s may influence how legal battles over this kind of data scraping play out. As Ars’ Timothy Lee and James Grimmelmann noted in a recent legal analysis, the establishment of a settled licensing market can have a huge impact on whether courts consider a novel use of digitized data to be “fair use” under copyright law.

“The more [AI data licensing] deals like this are signed in the coming months, the easier it will be for the plaintiffs to argue that the ‘effect on the market’ prong of fair use analysis should take this licensing market into account,” Lee and Grimmelmann wrote.

And while Reddit sees LLMs as a new revenue opportunity, the site also sees their popularity as a potential threat. The S-1 filing notes that “some users are also turning to LLMs such as ChatGPT, Gemini, and Anthropic” for seeking information, putting them in the same category of Reddit competition as “Google, Amazon, YouTube, Wikipedia, X, and other news sites.”

After filing for its IPO in late 2021, reports suggest Reddit is aiming to hit the stock market next month officially. The company will offer users and moderators with sufficient karma and/or activity on the site the opportunity to participate in that IPO through a directed share program.

Advance Publications, which owns Ars Technica parent Condé Nast, is the largest shareholder of Reddit.

Reddit cashes in on AI gold rush with $203M in LLM training license fees Read More »

Just 10 lines of code can steal AI secrets from Apple, AMD, and Qualcomm GPUs

AI, data leaks, GPU, LLMs, Security, syndication / Mike M. / January 17, 2024

massive leakage —

Patching all affected devices, which include some Macs and iPhones, may be tough.

Lily Hay Newman, wired.com – Jan 17, 2024 6: 15 pm UTC

As more companies ramp up development of artificial intelligence systems, they are increasingly turning to graphics processing unit (GPU) chips for the computing power they need to run large language models (LLMs) and to crunch data quickly at massive scale. Between video game processing and AI, demand for GPUs has never been higher, and chipmakers are rushing to bolster supply. In new findings released today, though, researchers are highlighting a vulnerability in multiple brands and models of mainstream GPUs—including Apple, Qualcomm, and AMD chips—that could allow an attacker to steal large quantities of data from a GPU’s memory.

The silicon industry has spent years refining the security of central processing units, or CPUs, so they don’t leak data in memory even when they are built to optimize for speed. However, since GPUs were designed for raw graphics processing power, they haven’t been architected to the same degree with data privacy as a priority. As generative AI and other machine learning applications expand the uses of these chips, though, researchers from New York-based security firm Trail of Bits say that vulnerabilities in GPUs are an increasingly urgent concern.

“There is a broader security concern about these GPUs not being as secure as they should be and leaking a significant amount of data,” Heidy Khlaaf, Trail of Bits’ engineering director for AI and machine learning assurance, tells WIRED. “We’re looking at anywhere from 5 megabytes to 180 megabytes. In the CPU world, even a bit is too much to reveal.”

To exploit the vulnerability, which the researchers call LeftoverLocals, attackers would need to already have established some amount of operating system access on a target’s device. Modern computers and servers are specifically designed to silo data so multiple users can share the same processing resources without being able to access each others’ data. But a LeftoverLocals attack breaks down these walls. Exploiting the vulnerability would allow a hacker to exfiltrate data they shouldn’t be able to access from the local memory of vulnerable GPUs, exposing whatever data happens to be there for the taking, which could include queries and responses generated by LLMs as well as the weights driving the response.

In their proof of concept, as seen in the GIF below, the researchers demonstrate an attack where a target—shown on the left—asks the open source LLM Llama.cpp to provide details about WIRED magazine. Within seconds, the attacker’s device—shown on the right—collects the majority of the response provided by the LLM by carrying out a LeftoverLocals attack on vulnerable GPU memory. The attack program the researchers created uses less than 10 lines of code.

An attacker (right) exploits the LeftoverLocals vulnerability to listen to LLM conversations.

Last summer, the researchers tested 11 chips from seven GPU makers and multiple corresponding programming frameworks. They found the LeftoverLocals vulnerability in GPUs from Apple, AMD, and Qualcomm and launched a far-reaching coordinated disclosure of the vulnerability in September in collaboration with the US-CERT Coordination Center and the Khronos Group, a standards body focused on 3D graphics, machine learning, and virtual and augmented reality.

The researchers did not find evidence that Nvidia, Intel, or Arm GPUs contain the LeftoverLocals vulnerability, but Apple, Qualcomm, and AMD all confirmed to WIRED that they are impacted. This means that well-known chips like the AMD Radeon RX 7900 XT and devices like Apple’s iPhone 12 Pro and M2 MacBook Air are vulnerable. The researchers did not find the flaw in the Imagination GPUs they tested, but others may be vulnerable.

Just 10 lines of code can steal AI secrets from Apple, AMD, and Qualcomm GPUs Read More »

NY Times copyright suit wants OpenAI to delete all GPT instances

AI, copyright, infringement, lawsuit, LLMs, microsoft, ny times, openai, Policy / Shannon Garcia / December 28, 2023

Not the sincerest form of flattery —

Shows evidence that GPT-based systems will reproduce Times articles if asked.

John Timmer – Dec 27, 2023 7: 05 pm UTC

Image of a CPU on a motherboard with — Enlarge / Microsoft is named in the suit for allegedly building the system that allowed GPT derivatives to be trained using infringing material.

In August, word leaked out that The New York Times was considering joining the growing legion of creators that are suing AI companies for misappropriating their content. The Times had reportedly been negotiating with OpenAI regarding the potential to license its material, but those talks had not gone smoothly. So, eight months after the company was reportedly considering suing, the suit has now been filed.

The Times is targeting various companies under the OpenAI umbrella, as well as Microsoft, an OpenAI partner that both uses it to power its Copilot service and helped provide the infrastructure for training the GPT Large Language Model. But the suit goes well beyond the use of copyrighted material in training, alleging that OpenAI-powered software will happily circumvent the Times’ paywall and ascribe hallucinated misinformation to the Times.

Journalism is expensive

The suit notes that The Times maintains a large staff that allows it to do things like dedicate reporters to a huge range of beats and engage in important investigative journalism, among other things. Because of those investments, the newspaper is often considered an authoritative source on many matters.

All of that costs money, and The Times earns that by limiting access to its reporting through a robust paywall. In addition, each print edition has a copyright notification, the Times’ terms of service limit the copying and use of any published material, and it can be selective about how it licenses its stories. In addition to driving revenue, these restrictions also help it to maintain its reputation as an authoritative voice by controlling how its works appear.

The suit alleges that OpenAI-developed tools undermine all of that. “By providing Times content without The Times’s permission or authorization, Defendants’ tools undermine and damage The Times’s relationship with its readers and deprive The Times of subscription, licensing, advertising, and affiliate revenue,” the suit alleges.

Part of the unauthorized use The Times alleges came during the training of various versions of GPT. Prior to GPT-3.5, information about the training dataset was made public. One of the sources used is a large collection of online material called “Common Crawl,” which the suit alleges contains information from 16 million unique records from sites published by The Times. That places the Times as the third most referenced source, behind Wikipedia and a database of US patents.

OpenAI no longer discloses as many details of the data used for training of recent GPT versions, but all indications are that full-text NY Times articles are still part of that process (Much more on that in a moment.) Expect access to training information to be a major issue during discovery if this case moves forward.

Not just training

A number of suits have been filed regarding the use of copyrighted material during training of AI systems. But the Times’ suit goes well beyond that to show how the material ingested during training can come back out during use. “Defendants’ GenAI tools can generate output that recites Times content verbatim, closely summarizes it, and mimics its expressive style, as demonstrated by scores of examples,” the suit alleges.

The suit alleges—and we were able to verify—that it’s comically easy to get GPT-powered systems to offer up content that is normally protected by the Times’ paywall. The suit shows a number of examples of GPT-4 reproducing large sections of articles nearly verbatim.

The suit includes screenshots of ChatGPT being given the title of a piece at The New York Times and asked for the first paragraph, which it delivers. Getting the ensuing text is apparently as simple as repeatedly asking for the next paragraph.

ChatGPT has apparently closed that loophole in between the preparation of that suit and the present. We entered some of the prompts shown in the suit, and were advised “I recommend checking The New York Times website or other reputable sources,” although we can’t rule out that context provided prior to that prompt could produce copyrighted material.

Ask for a paragraph, and Copilot will hand you a wall of normally paywalled text.

John Timmer

But not all loopholes have been closed. The suit also shows output from Bing Chat, since rebranded as Copilot. We were able to verify that asking for the first paragraph of a specific article at The Times caused Copilot to reproduce the first third of the article.

The suit is dismissive of attempts to justify this as a form of fair use. “Publicly, Defendants insist that their conduct is protected as ‘fair use’ because their unlicensed use of copyrighted content to train GenAI models serves a new ‘transformative’ purpose,” the suit notes. “But there is nothing ‘transformative’ about using The Times’s content without payment to create products that substitute for The Times and steal audiences away from it.”

Reputational and other damages

The hallucinations common to AI also came under fire in the suit for potentially damaging the value of the Times’ reputation, and possibly damaging human health as a side effect. “A GPT model completely fabricated that “The New York Times published an article on January 10, 2020, titled ‘Study Finds Possible Link between Orange Juice and Non-Hodgkin’s Lymphoma,’” the suit alleges. “The Times never published such an article.”

Similarly, asking about a Times article on heart-healthy foods allegedly resulted in Copilot saying it contained a list of examples (which it didn’t). When asked for the list, 80 percent of the foods on weren’t even mentioned by the original article. In another case, recommendations were ascribed to the Wirecutter when the products hadn’t even been reviewed by its staff.

As with the Times material, it’s alleged that it’s possible to get Copilot to offer up large chunks of Wirecutter articles (The Wirecutter is owned by The New York Times). But the suit notes that these article excerpts have the affiliate links stripped out of them, keeping the Wirecutter from its primary source of revenue.

The suit targets various OpenAI companies for developing the software, as well as Microsoft—the latter for both offering OpenAI-powered services, and for having developed the computing systems that enabled the copyrighted material to be ingested during training. Allegations include direct, contributory, and vicarious copyright infringement, as well as DMCA and trademark violations. Finally, it alleges “Common Law Unfair Competition By Misappropriation.”

The suit seeks nothing less than the erasure of both any GPT instances that the parties have trained using material from the Times, as well as the destruction of the datasets that were used for the training. It also asks for a permanent injunction to prevent similar conduct in the future. The Times also wants money, lots and lots of money: “statutory damages, compensatory damages, restitution, disgorgement, and any other relief that may be permitted by law or equity.”

NY Times copyright suit wants OpenAI to delete all GPT instances Read More »

Apple wants AI to run directly on its hardware instead of in the cloud

AI, Apple, IPhone, LLMs, syndication / DJ Henderson / December 21, 2023

Making Siri smarter —

iPhone maker wants to catch up to its rivals when it comes to AI.

Tim Bradshaw, Financial Times – Dec 21, 2023 2: 43 pm UTC

Apple’s latest research about running large language models on smartphones offers the clearest signal yet that the iPhone maker plans to catch up with its Silicon Valley rivals in generative artificial intelligence.

The paper, entitled “LLM in a Flash,” offers a “solution to a current computational bottleneck,” its researchers write.

Its approach “paves the way for effective inference of LLMs on devices with limited memory,” they said. Inference refers to how large language models, the large data repositories that power apps like ChatGPT, respond to users’ queries. Chatbots and LLMs normally run in vast data centers with much greater computing power than an iPhone.

The paper was published on December 12 but caught wider attention after Hugging Face, a popular site for AI researchers to showcase their work, highlighted it late on Wednesday. It is the second Apple paper on generative AI this month and follows earlier moves to enable image-generating models such as Stable Diffusion to run on its custom chips.

Device manufacturers and chipmakers are hoping that new AI features will help revive the smartphone market, which has had its worst year in a decade, with shipments falling an estimated 5 percent, according to Counterpoint Research.

Despite launching one of the first virtual assistants, Siri, back in 2011, Apple has been largely left out of the wave of excitement about generative AI that has swept through Silicon Valley in the year since OpenAI launched its breakthrough chatbot ChatGPT. Apple has been viewed by many in the AI community as lagging behind its Big Tech rivals, despite hiring Google’s top AI executive, John Giannandrea, in 2018.

While Microsoft and Google have largely focused on delivering chatbots and other generative AI services over the Internet from their vast cloud computing platforms, Apple’s research suggests that it will instead focus on AI that can run directly on an iPhone.

Apple’s rivals, such as Samsung, are gearing up to launch a new kind of “AI smartphone” next year. Counterpoint estimated more than 100 million AI-focused smartphones would be shipped in 2024, with 40 percent of new devices offering such capabilities by 2027.

The head of the world’s largest mobile chipmaker, Qualcomm chief executive Cristiano Amon, forecast that bringing AI to smartphones would create a whole new experience for consumers and reverse declining mobile sales.

“You’re going to see devices launch in early 2024 with a number of generative AI use cases,” he told the Financial Times in a recent interview. “As those things get scaled up, they start to make a meaningful change in the user experience and enable new innovation which has the potential to create a new upgrade cycle in smartphones.”

More sophisticated virtual assistants will be able to anticipate users’ actions such as texting or scheduling a meeting, he said, while devices will also be capable of new kinds of photo editing techniques.

Google this month unveiled a version of its new Gemini LLM that will run “natively” on its Pixel smartphones.

Running the kind of large AI model that powers ChatGPT or Google’s Bard on a personal device brings formidable technical challenges, because smartphones lack the huge computing resources and energy available in a data center. Solving this problem could mean that AI assistants respond more quickly than they do from the cloud and even work offline.

Ensuring that queries are answered on an individual’s own device without sending data to the cloud is also likely to bring privacy benefits, a key differentiator for Apple in recent years.

“Our experiment is designed to optimize inference efficiency on personal devices,” its researchers said. Apple tested its approach on models including Falcon 7B, a smaller version of an open source LLM originally developed by the Technology Innovation Institute in Abu Dhabi.

Optimizing LLMs to run on battery-powered devices has been a growing focus for AI researchers. Academic papers are not a direct indicator of how Apple intends to add new features to its products, but they offer a rare glimpse into its secretive research labs and the company’s latest technical breakthroughs.

“Our work not only provides a solution to a current computational bottleneck but also sets a precedent for future research,” wrote Apple’s researchers in the conclusion to their paper. “We believe as LLMs continue to grow in size and complexity, approaches like this work will be essential for harnessing their full potential in a wide range of devices and applications.”

Apple did not immediately respond to a request for comment.

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