How to run an LLM on your laptop

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How to run an LLM on your laptop

MIT Technology Review’s How To series helps you get things done. Simon Willison has a plan for the end of the world. It’s a USB stick, onto which he has loaded a couple of his favorite open-weight LLMs—models that have been shared publicly by their creators and that can, in principle, be downloaded and run with local hardware. If human civilization should ever collapse, Willison plans to use all the knowledge encoded in their billions of parameters for help. “It’s like having a weird, condensed, faulty version of Wikipedia, so I can help reboot society with the help of my little USB stick,” he says. But you don’t need to be planning for the end of the world to want to run an LLM on your own device. Willison, who writes a popular blog about local LLMs and software development, has plenty of compatriots: r/LocalLLaMA, a subreddit devoted to running LLMs on your own hardware, has half a million members. For people who are concerned about privacy, want to break free from the control of the big LLM companies, or just enjoy tinkering, local models offer a compelling alternative to ChatGPT and its web-based peers. The local LLM world used to have a high barrier to entry: In the early days, it was impossible to run anything useful without investing in pricey GPUs. But researchers have had so much success in shrinking down and speeding up models that anyone with a laptop, or even a smartphone, can now get in on the action. “A couple of years ago, I’d have said personal computers are not powerful enough to run the good models. You need a $50,000 server rack to run them,” Willison says. “And I kept on being proved wrong time and time again.” Why you might want to download your own LLM Getting into local models takes a bit more effort than, say, navigating to ChatGPT’s online interface. But the very accessibility of a tool like ChatGPT comes with a cost. “It’s the classic adage: If something’s free, you’re the product,” says Elizabeth Seger, the director of digital policy at Demos, a London-based think tank. OpenAI, which offers both paid and free tiers, trains its models on users’ chats by default. It’s not too difficult to opt out of this training, and it also used to be possible to remove your chat data from OpenAI’s systems entirely, until a recent legal decision in the New York Times’ ongoing lawsuit against OpenAI required the company to maintain all user conversations with ChatGPT. Google, which has access to a wealth of data about its users, also trains its models on both free and paid users’ interactions with Gemini, and the only way to opt out of that training is to set your chat history to delete automatically—which means that you also lose access to your previous conversations. In general, Anthropic does not train its models using user conversations, but it will train on conversations that have been “flagged for Trust & Safety review.” Training may present particular privacy risks because of the ways that models internalize, and often recapitulate, their training data. Many people trust LLMs with deeply personal conversations—but if models are trained on that data, those conversations might not be nearly as private as users think, according to some experts. “Some of your personal stories may be cooked into some of the models, and eventually be spit out in bits and bytes somewhere to other people,” says Giada Pistilli, principal ethicist at the company Hugging Face, which runs a huge library of freely downloadable LLMs and other AI resources. For Pistilli, opting for local models as opposed to online chatbots has implications beyond privacy. “Technology means power,” she says. “And so who[ever] owns the technology also owns the power.” States, organizations, and even individuals might be motivated to disrupt the concentration of AI power in the hands of just a few companies by running their own local models. Breaking away from the big AI companies also means having more control over your LLM experience. Online LLMs are constantly shifting under users’ feet: Back in April, ChatGPT suddenly started sucking up to users far more than it had previously, and just last week Grok started calling itself MechaHitler on X. Providers tweak their models with little warning, and while those tweaks might sometimes improve model performance, they can also cause undesirable behaviors. Local LLMs may have their quirks, but at least they are consistent. The only person who can change your local model is you. Of course, any model that can fit on a personal computer is going to be less powerful than the premier online offerings from the major AI companies. But there’s a benefit to working with weaker models—they can inoculate you against the more pernicious limitations of their larger peers. Small models may, for example, hallucinate more frequently and more obviously than Claude, GPT, and Gemini, and seeing those hallucinations can help you build up an awareness of how and when the larger models might also lie. “Running local models is actually a really good exercise for developing that broader intuition for what these things can do,” Willison says. How to get started Local LLMs aren’t just for proficient coders. If you’re comfortable using your computer’s command-line interface, which allows you to browse files and run apps using text prompts, Ollama is a great option. Once you’ve installed the software, you can download and run any of the hundreds of models they offer with a single command. If you don’t want to touch anything that even looks like code, you might opt for LM Studio, a user-friendly app that takes a lot of the guesswork out of running local LLMs. You can browse models from Hugging Face from right within the app, which provides plenty of information to help you make the right choice. Some popular and widely used models are tagged as “Staff Picks,” and every model is labeled according to whether it can be run entirely on your machine’s speedy GPU, needs to be shared between your GPU and slower CPU, or is too big to fit onto your device at all. Once you’ve chosen a model, you can download it, load it up, and start interacting with it using the app’s chat interface. As you experiment with different models, you’ll start to get a feel for what your machine can handle. According to Willison, every billion model parameters require about one GB of RAM to run, and I found that approximation to be accurate: My own 16 GB laptop managed to run Alibaba’s Qwen3 14B as long as I quit almost every other app. If you run into issues with speed or usability, you can always go smaller—I got reasonable responses from Qwen3 8B as well. And if you go really small, you can even run models on your cell phone. My beat-up iPhone 12 was able to run Meta’s Llama 3.2 1B using an app called LLM Farm. It’s not a particularly good model—it very quickly goes off into bizarre tangents and hallucinates constantly—but trying to coax something so chaotic toward usability can be entertaining. If I’m ever on a plane sans Wi-Fi and desperate for a probably false answer to a trivia question, I now know where to look. Some of the models that I was able to run on my laptop were effective enough that I can imagine using them in my journalistic work. And while I don’t think I’ll depend on phone-based models for anything anytime soon, I really did enjoy playing around with them. “I think most people probably don’t need to do this, and that’s fine,” Willison says. “But for the people who want to do this, it’s so much fun.”

MIT Technology Review’s How To series helps you get things done.

Simon Willison has a plan for the end of the world. It’s a USB stick, onto which he has loaded a couple of his favorite open-weight LLMs—models that have been shared publicly by their creators and that can, in principle, be downloaded and run with local hardware. If human civilization should ever collapse, Willison plans to use all the knowledge encoded in their billions of parameters for help. “It’s like having a weird, condensed, faulty version of Wikipedia, so I can help reboot society with the help of my little USB stick,” he says.

But you don’t need to be planning for the end of the world to want to run an LLM on your own device. Willison, who writes a popular blog about local LLMs and software development, has plenty of compatriots: r/LocalLLaMA, a subreddit devoted to running LLMs on your own hardware, has half a million members.

For people who are concerned about privacy, want to break free from the control of the big LLM companies, or just enjoy tinkering, local models offer a compelling alternative to ChatGPT and its web-based peers.

The local LLM world used to have a high barrier to entry: In the early days, it was impossible to run anything useful without investing in pricey GPUs. But researchers have had so much success in shrinking down and speeding up models that anyone with a laptop, or even a smartphone, can now get in on the action. “A couple of years ago, I’d have said personal computers are not powerful enough to run the good models. You need a $50,000 server rack to run them,” Willison says. “And I kept on being proved wrong time and time again.”

Why you might want to download your own LLM

Getting into local models takes a bit more effort than, say, navigating to ChatGPT’s online interface. But the very accessibility of a tool like ChatGPT comes with a cost. “It’s the classic adage: If something’s free, you’re the product,” says Elizabeth Seger, the director of digital policy at Demos, a London-based think tank.

OpenAI, which offers both paid and free tiers, trains its models on users’ chats by default. It’s not too difficult to opt out of this training, and it also used to be possible to remove your chat data from OpenAI’s systems entirely, until a recent legal decision in the New York Times’ ongoing lawsuit against OpenAI required the company to maintain all user conversations with ChatGPT.

Google, which has access to a wealth of data about its users, also trains its models on both free and paid users’ interactions with Gemini, and the only way to opt out of that training is to set your chat history to delete automatically—which means that you also lose access to your previous conversations. In general, Anthropic does not train its models using user conversations, but it will train on conversations that have been “flagged for Trust & Safety review.”

Training may present particular privacy risks because of the ways that models internalize, and often recapitulate, their training data. Many people trust LLMs with deeply personal conversations—but if models are trained on that data, those conversations might not be nearly as private as users think, according to some experts.

“Some of your personal stories may be cooked into some of the models, and eventually be spit out in bits and bytes somewhere to other people,” says Giada Pistilli, principal ethicist at the company Hugging Face, which runs a huge library of freely downloadable LLMs and other AI resources.

For Pistilli, opting for local models as opposed to online chatbots has implications beyond privacy. “Technology means power,” she says. “And so who[ever] owns the technology also owns the power.” States, organizations, and even individuals might be motivated to disrupt the concentration of AI power in the hands of just a few companies by running their own local models.

Breaking away from the big AI companies also means having more control over your LLM experience. Online LLMs are constantly shifting under users’ feet: Back in April, ChatGPT suddenly started sucking up to users far more than it had previously, and just last week Grok started calling itself MechaHitler on X.

Providers tweak their models with little warning, and while those tweaks might sometimes improve model performance, they can also cause undesirable behaviors. Local LLMs may have their quirks, but at least they are consistent. The only person who can change your local model is you.

Of course, any model that can fit on a personal computer is going to be less powerful than the premier online offerings from the major AI companies. But there’s a benefit to working with weaker models—they can inoculate you against the more pernicious limitations of their larger peers. Small models may, for example, hallucinate more frequently and more obviously than Claude, GPT, and Gemini, and seeing those hallucinations can help you build up an awareness of how and when the larger models might also lie.

“Running local models is actually a really good exercise for developing that broader intuition for what these things can do,” Willison says.

How to get started

Local LLMs aren’t just for proficient coders. If you’re comfortable using your computer’s command-line interface, which allows you to browse files and run apps using text prompts, Ollama is a great option. Once you’ve installed the software, you can download and run any of the hundreds of models they offer with a single command.

If you don’t want to touch anything that even looks like code, you might opt for LM Studio, a user-friendly app that takes a lot of the guesswork out of running local LLMs. You can browse models from Hugging Face from right within the app, which provides plenty of information to help you make the right choice. Some popular and widely used models are tagged as “Staff Picks,” and every model is labeled according to whether it can be run entirely on your machine’s speedy GPU, needs to be shared between your GPU and slower CPU, or is too big to fit onto your device at all. Once you’ve chosen a model, you can download it, load it up, and start interacting with it using the app’s chat interface.

As you experiment with different models, you’ll start to get a feel for what your machine can handle. According to Willison, every billion model parameters require about one GB of RAM to run, and I found that approximation to be accurate: My own 16 GB laptop managed to run Alibaba’s Qwen3 14B as long as I quit almost every other app. If you run into issues with speed or usability, you can always go smaller—I got reasonable responses from Qwen3 8B as well.

And if you go really small, you can even run models on your cell phone. My beat-up iPhone 12 was able to run Meta’s Llama 3.2 1B using an app called LLM Farm. It’s not a particularly good model—it very quickly goes off into bizarre tangents and hallucinates constantly—but trying to coax something so chaotic toward usability can be entertaining. If I’m ever on a plane sans Wi-Fi and desperate for a probably false answer to a trivia question, I now know where to look.

Some of the models that I was able to run on my laptop were effective enough that I can imagine using them in my journalistic work. And while I don’t think I’ll depend on phone-based models for anything anytime soon, I really did enjoy playing around with them. “I think most people probably don’t need to do this, and that’s fine,” Willison says. “But for the people who want to do this, it’s so much fun.”

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Each month Data Center Frontier, in partnership with Pkaza, posts some of the hottest data center career opportunities in the market. Here’s a look at some of the latest data center jobs posted on the Data Center Frontier jobs board, powered by Pkaza Critical Facilities Recruiting. Peter Kazella of Pkaza Critical Facilities Recruiting provides tips for building a healthy applicant pool. Switchgear Field Service Technician – Critical Facilities Nationwide Travel This position is also available in any major data center region: Ashburn, VA; Charlotte, NC; Atlanta, GA; Denver, CO; Portland, OR; Seattle, WA; Las Vegas, NV; or Phoenix, AZ. Multiple opportunities for both senior and mid-level switchgear field service technicians. These openings are with a nationwide market leader of power distribution solutions, specializing in switchgear and controls for mission-critical environments. This company provides customized solutions for enterprise, colocation, and hyperscale data centers, ensuring reliability and uptime through controls integration, power distribution solutions, and switchgear installations. Their services include installations, retrofits, upgrades, turnkey electrical solutions, and preventive & corrective maintenance of UPS, switchgear, generators, and PLC systems. This is an excellent career-growth opportunity to work on exciting projects with leading-edge technology and competitive compensation. Electrical Commissioning Engineer New Albany, OH This traveling position is also available in: Richmond, VA; Ashburn, VA; Charlotte, NC; Atlanta, GA; Hampton, GA; Fayetteville, GA; Minneapolis, MN; Phoenix, AZ; Dallas, TX; or Chicago, IL. *** ALSO looking for a LEAD EE and ME CxA agents and CxA PMs *** Our client is an engineering design and commissioning company that has a national footprint and specializes in MEP critical facilities design. They provide design, commissioning, consulting and management expertise in the critical facilities space. They have a mindset to provide reliability, energy efficiency, sustainable design and LEED expertise when providing these consulting services for enterprise, colocation and hyperscale companies. This career-growth

DCF Trends Summit 2025: Power Chat

Data Center Frontier Editor in Chief Matt Vincent and Contributing Editor Bill Kleyman (CEO/Apolo) recently had another video chat to discuss the Data Center Frontier Trends Summit 2025 and, in particular, the event’s focus on data center power. The second annual Trends Summit is scheduled for August 26-28 in Reston, Virginia. Register Now This second QuickChat in our series—following the opening discussion that set the stage for the conference—now zeroes in on data center power challenges. The conversation explores how AI’s exponential growth is straining power infrastructure, turning energy into both a critical market-entry barrier and a defining theme of the event. The Power Challenge: Quantifying the Scale Kleyman outlined the sheer scale of the power challenge facing the industry. He cited a Goldman Sachs report projecting that U.S. data center power consumption, currently around 2-3% of total consumption, could more than double to 8-9% by 2028. He also highlighted a forecast that the data center industry will require upwards of 50 gigawatts (GW) of power by 2035, a figure he put into perspective by noting that 1 GW can power a city of a million people. The discussion mentions a few large-scale projects that illustrate this trend: Kevin O’Leary‘s Wonder Valley project in Alberta, Canada, aiming for 8 GW. Tract’s new multi-gigawatt campus project in Texas amid plans for a staggering 25 GW national build-out. Kleyman also referenced the NVL576 rack unveiled at Nvidia GTC 2025, which is capable of supporting 600 kilowatts (kW) per rack, signaling the official arrival of the “megawatt era class of data center racks.” Grid-Optional Solutions and the Summit Panels The discussion transitioned to how the industry is moving from being solely grid-reliant to “grid-optional.” A significant trend highlighted is the inclusion of behind-the-meter power generation in new builds, with 30% of new U.S. data

Earnings Frontier: Equinix and Modine

Welcome to Earnings Frontier, our new series where we cut through the noise of quarterly financial reports to get straight to what matters for the data center industry. Today, the spotlight is on a pair of companies whose recent earnings calls offered valuable insights into the market’s current trajectory: Equinix and Modine. On one hand, we have the colocation giant, Equinix, which continues to demonstrate its enduring strength as a foundational pillar of the digital economy. Their latest results underscore a familiar story of robust customer engagement, strategic capacity expansion, and a steadfast focus on interconnection. The numbers speak to the company’s ability to capitalize on the sustained demand for cloud and AI infrastructure, all while successfully navigating a complex and competitive landscape. Then there’s Modine, a company whose name might not be as synonymous with data centers as Equinix’s, but whose recent performance is a powerful testament to the critical role of cooling in the age of AI. Their earnings call highlighted a strategic pivot toward data center cooling technologies, with strong growth in their Climate Solutions segment. The company’s commentary reveals a deep understanding of the market’s evolving needs, with a keen focus on high-efficiency, advanced cooling strategies that are becoming non-negotiable for next-generation workloads. In the following segments, we’ll dive into the details of these two contrasting, yet equally illuminating, earnings reports, exploring the key metrics, strategic takeaways, and forward-looking statements that are shaping the future of digital infrastructure. Equinix Outlines Three-Pronged Strategy for 2025 Double-Digit Revenue Growth CEO Adaire Fox-Martin described growth from acquisitions, new projects, customer AI adoption, and long-term strategies, saying, “We were built for this moment.” Key Takeaways Leaders at Equinix, Inc. announced the acquisition of three data centers in Manila and has 59 projects in 34 metro areas, including key markets such

Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs). In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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