Latham & Watkins has made a landmark move in BigLaw by purchasing its own Nvidiaa-powered AI servers, becoming the first major law firm to publicly own and operate the hardware necessary to build and customise artificial intelligence models in-house. The U.S. legal giant has acquired several Nvidia servers, each outfitted with multiple GPUs that deliver the immense computing power required to train and run sophisticated AI systems. Rather than remaining solely dependent on external technology vendors, Latham’s own engineering team is now using this infrastructure to tailor existing AI models specifically to the firm’s operational and client-service needs. This strategic shift represents a significant departure from the traditional law firm model of outsourcing technological capabilities, positioning Latham as a pioneer in the race to control the legal AI stack from the ground up.
What Are Open-Weight AI Models and Why Do They Matter for Law Firms?
For those less versed in technical terminology, the models Latham is working with are known as “open-weight” AI models. These are artificial intelligence systems whose pre-trained parameters — or “weights” — are publicly released by their developers, allowing organisations to download, customise, and run them on their own hardware. This stands in contrast to proprietary, closed models like the consumer versions of ChatGPT, where users interact through an API or interface but cannot modify the underlying technology or control where data is processed.
Open-weight models give firms like Latham the ability to fine-tune AI for specific legal tasks — such as contract analysis, due diligence, or regulatory research — without sharing sensitive data with a third-party cloud provider. The firm can continue using familiar tools like ChatGPT, Claude, and Gemini for less sensitive work, but now has an in-house alternative for matters that require the highest levels of confidentiality and customisation.
Latham’s Chief Information Officer on Why the Firm Bought Its Own AI Servers
Speaking to the Financial Times, Latham chief information officer Rene Mendoza made clear that data security was a primary driver behind the decision. “Sometimes we may have information that is so sensitive, client information that we really want to protect, we don’t want to put it to any cloud vendor,” Mendoza said. By keeping its AI infrastructure on-premises, Latham ensures that particularly sensitive client information never leaves its own systems, eliminating the risk of exposure through external cloud environments. Mendoza also highlighted the strategic advantage of vendor independence: “We are not hitching our wagon to one particular company.” This flexibility allows Latham to adapt its AI capabilities as the technology evolves, without being locked into a single provider’s ecosystem, pricing model, or roadmap.
How Does the Latham AI Server Setup Compare to Other BigLaw Technology Strategies?
Latham’s approach puts it out in front among the legal heavyweights, but it is far from the only major law firm investing heavily in artificial intelligence. Other firms have chosen different paths, each reflecting their own strategic priorities and risk tolerances. Kirkland & Ellis has set aside $500 million to build its own AI tools, working closely with the data analytics company Palantir to develop custom solutions. A&O Shearman has formed a partnership with Harvey, a legal AI startup that has rapidly gained traction in the industry. Freshfields, meanwhile, announced a partnership with Anthropic earlier this year to build legal AI tools on top of that company’s models.
Each of these strategies has its own merits, but Latham’s decision to buy and operate its own Nvidia servers — rather than relying on a cloud provider or a single AI startup — represents the most hands-on, capital-intensive approach yet seen in BigLaw. By controlling the hardware, the software, and the data pipeline in-house, Latham gains maximum flexibility and security, though at a significant upfront and ongoing cost.
The Scale of Latham’s AI Investment: Staff, Infrastructure, and Cost
The undertaking is anything but small. Latham currently employs more than 900 technology specialists, a workforce that dwarfs the IT departments of most law firms and even some mid-sized technology companies. The firm’s new Nvidia servers are housed in locked data-centre space accessible only by Latham’s own staff, underscoring the physical security measures being taken to protect both the hardware and the data it processes.
Latham has not publicly disclosed the total cost of the server purchase and ongoing maintenance, but the scale of the operation offers some clues. A setup of this magnitude — including the high-end Nvidia GPUs, specialised networking equipment, cooling and power infrastructure, and the expert staff needed to operate and maintain it — can easily run into tens of millions of dollars per year. This level of investment signals that Latham views AI not as a peripheral experiment, but as a core strategic capability that will define the firm’s competitive position for years to come.
How Latham’s AI Strategy Is Reshaping the Firm’s Talent and Culture
The technological transformation is also having a profound effect on the firm’s internal composition. Michael Rubin, a Latham partner and chair of the firm’s AI strategy committee, noted that the number of technology specialists, innovation lawyers, and AI-focused roles has increased significantly in recent years. This shift means Latham is not only changing how it uses technology, but also who it hires and how it structures its teams. Innovation lawyers — legal professionals who combine legal expertise with a deep understanding of technology and product development — are becoming increasingly central to the firm’s operations, bridging the gap between the legal practice and the engineering team.
This evolution in talent strategy mirrors the broader trend in the legal industry, where the line between law firm and technology company is blurring. Firms that can attract and retain top technical talent — engineers, data scientists, machine learning specialists — are likely to have a significant advantage in the AI-driven future of legal services.
Why Did Latham Choose Nvidia Servers and What Does This Mean for the Legal AI Market?
Nvidia’s GPUs have become the de facto standard for training and running large AI models, thanks to their parallel processing capabilities that far exceed traditional CPUs for this type of workload. By choosing Nvidia hardware, Latham is aligning itself with the same technology stack used by the world’s leading AI labs and cloud providers. This gives the firm access to the most powerful and efficient computing hardware available for AI workloads, while also ensuring compatibility with the broad ecosystem of open-weight models that are optimised for Nvidia’s architecture.
The decision to invest in its own hardware rather than relying on cloud-based GPU instances from Amazon Web Services, Microsoft Azure, or Google Cloud has several implications. For Latham, it means predictable long-term costs once the initial investment is made, rather than variable cloud bills that can spike unpredictably. It also gives the firm complete control over hardware utilisation, allowing engineers to run experiments and fine-tune models around the clock without worrying about cloud instance availability or vendor-imposed limits.
For the broader legal AI market, Latham’s move signals that the technology has matured to the point where on-premises deployment is a viable and attractive option for large firms. This could prompt other firms to reconsider their own AI strategies, potentially accelerating investment in private infrastructure. It also puts pressure on AI vendors and cloud providers to offer more compelling value propositions for law firms that might otherwise choose to go it alone.
What Are the Risks and Challenges of Latham’s Approach to AI?
While Latham’s strategy offers significant advantages, it is not without risks and challenges. Operating a fleet of high-performance AI servers requires specialised expertise that is in short supply and expensive to recruit. The firm must also manage the ongoing costs of hardware maintenance, software updates, and energy consumption, all of which can add up to substantial annual expenses. Additionally, the pace of innovation in AI hardware means that today’s top-of-the-line Nvidia GPUs could be obsolete in two to three years, forcing the firm to make periodic capital investments to stay current.
There is also the risk that the open-weight models Latham is customising may not perform as well as the latest frontier models from OpenAI, Google, or Anthropic for certain tasks. The firm will need to carefully balance the benefits of in-house customisation and security against the potential performance gap with state-of-the-art cloud-based models. Mendoza’s comment that Latham is “not hitching our wagon to one particular company” suggests the firm plans to maintain a hybrid approach, using its own models where appropriate while continuing to leverage external tools for other use cases.
How Does Data Security Drive the Decision to Keep AI In-House?
For a law firm handling some of the world’s most sensitive corporate transactions, litigation, and regulatory matters, data security is paramount. The decision to keep AI processing on-premises is fundamentally about control. When client data is sent to a cloud provider’s AI service, even with strong contractual protections, it necessarily passes through systems that the firm does not directly control. By running models on its own Nvidia servers, Latham ensures that all data processing happens within its own secure environment, subject to its own security policies and access controls.
This is particularly important for clients in highly regulated industries such as healthcare, finance, and defence, where data sovereignty and confidentiality requirements are especially strict. Latham’s ability to offer AI-powered services without any data leaving the firm’s own systems could become a significant competitive differentiator in winning and retaining such clients.
What Does the Future Hold for Latham’s In-House AI Capabilities?
Latham’s investment in its own Nvidia-powered AI infrastructure is unlikely to be a one-time event. As the technology continues to evolve, the firm will need to expand its hardware capacity, update its models, and continuously refine its approach to AI development. The growing number of technology specialists and AI-focused roles within the firm suggests that Latham sees this as a long-term strategic commitment, not a short-term experiment.
The firm’s success with this approach could influence the entire legal industry. If Latham is able to demonstrate that on-premises AI offers a meaningful competitive advantage — in terms of both data security and model performance — other major law firms may be forced to follow suit. This could lead to a wave of capital investment in private AI infrastructure across BigLaw, reshaping the technology landscape of the legal profession. At the same time, the costs and complexity involved mean that smaller firms will likely continue to rely on cloud-based AI services, potentially widening the technology gap between the largest firms and the rest of the market.
For now, Latham has staked a bold claim as the first BigLaw firm to own and operate its own AI hardware. Whether this bet pays off will depend on the firm’s ability to effectively deploy these powerful tools in service of its clients, while managing the significant operational and financial demands that come with running a private AI infrastructure. The legal industry will be watching closely as Latham’s engineers begin to fine-tune their models and put them to work on real client matters, setting a precedent that could define the next era of legal technology.
