Technical Insight: Running Large Language Models on Commodity Hardware

Large Language Models (LLMs) like GPT-4 have taken the business world by storm. Yet many assume these powerful AI tools can only run in the cloud or on specialized supercomputers. In reality, a new trend is emerging: running LLMs on commodity hardware – the kind of servers and devices many companies already own or can easily acquire. Business leaders are paying attention because this approach promises greater privacy, regulatory compliance, and long-term cost savings. In this deep dive, we explore why organizations are bringing AI in-house, how they’re optimizing models for local deployment, and what trade-offs to consider. We’ll also share industry research and real-life examples of businesses gaining an edge with local AI.

The Shift Toward Local AI Solutions in Business

Enterprise adoption of AI is accelerating across the globe. A May 2024 McKinsey survey reported that 65% of organizations are now regularly using generative AI, nearly double the share from ten months prior (Getting Value from AI? The 2024 State of the Data Center Report Could Help). As AI use grows, where that AI runs has become a strategic question. Many companies initially embraced cloud AI platforms for their ease of use and scalability. But recent industry reports show a counter-trend: firms are increasingly interested in on-premises AI deployments. According to IDC, 53% of organizations prefer to develop AI models on-premises (with 49% preferring on-prem deployment), underscoring the importance of maintaining data control and low-latency access (On-Premises AI Infrastructure Balances Innovation and Security). In other words, over half of businesses would rather keep their AI in-house than rely exclusively on the cloud.

Why this shift? Concerns about data privacy, security, and compliance are major drivers. In IBM’s Global AI Adoption Index 2023, IT professionals at organizations not yet using generative AI cited data privacy (57%) and trust/transparency (43%) as their biggest blockers to adoption (IBM: While Enterprise Adoption of AI Increases, Barriers are Limiting Its Usage). It’s no surprise then that a separate 2023 survey found 75% of organizations worldwide are implementing or considering bans on tools like ChatGPT for workplace use (75% of Organizations Worldwide Set to Ban ChatGPT and Generative AI Apps on Work Devices). High-profile incidents – such as employees inadvertently leaking sensitive code to a public chatbot – have reinforced the risks of sending confidential data to third-party cloud AI services. Business leaders are realizing that running AI models locally can mitigate these risks while still reaping AI’s benefits.

Privacy, Compliance and Data Sovereignty

For many industries, keeping data on-premise isn’t just a preference – it’s often a compliance requirement. Sectors like finance, healthcare, and government deal with highly sensitive information and strict regulations about data handling. On-premise LLMs offer a significant advantage here: they allow organizations to retain complete control over their data. No customer records, financial data, or intellectual property needs to leave the company’s own servers. “On-premise LLMs provide a significant advantage in terms of data security and privacy. By keeping data within the company’s own infrastructure, organizations can ensure that sensitive information does not leave their secure environment,” notes a 2024 industry analysis (Road to On-Premise LLM Adoption - Part 1: Main Challenges with SaaS LLM Providers - Unit8). This level of control builds client trust and helps avoid legal pitfalls, since companies can more easily comply with data protection laws (such as GDPR or HIPAA) when the data stays under their roof.

Compliance and data sovereignty are especially critical for internationally regulated businesses. For example, financial institutions and healthcare providers often must process certain data on domestic servers. An on-premise AI deployment makes it easier to meet these obligations (Road to On-Premise LLM Adoption - Part 1: Main Challenges with SaaS LLM Providers - Unit8). If data residency laws prohibit exporting personal data to foreign cloud servers, a locally hosted model becomes the natural solution. We see this dynamic not only in regulated industries, but also in the public sector. Government agencies and defense contractors, for instance, are embracing “sovereign AI” – deploying large language models in secure on-site data centers isolated from the public internet. The motivation is clear: when privacy is paramount, local AI is the compliant path forward.

Running AI on commodity hardware can also reduce the “privacy tax” of cloud use. Businesses don’t have to worry about how a cloud provider might store or even inadvertently use their data. All AI processing occurs in an environment tightly controlled by the enterprise’s own IT team. This dramatically lowers the risk of data breaches. (It’s telling that tech-savvy companies like Samsung and Apple moved to restrict employee use of external AI tools after privacy incidents – preferring to explore internal solutions instead.) In short, keeping AI in-house keeps your data in-house – a simple principle with big implications for corporate risk management.

Cost Efficiency and ROI of On-Premise AI

Beyond privacy, cost is a pivotal factor driving local AI adoption. At first glance, cloud AI services seem convenient and cost-effective – you pay only for what you use, avoiding large upfront investments. However, as AI usage scales up, cloud costs can rise exponentially. Every query to an AI API, every document processed, or every training experiment incurs a fee. Many companies have learned that heavy reliance on cloud-based AI can lead to surprise bills. According to IDC, 60% of organizations surveyed believe developing and deploying AI on on-premises infrastructure is less expensive or about the same cost as using public cloud (On-Premises AI Infrastructure Balances Innovation and Security). This perception is increasingly backed by data.

Consider a recent cost-benefit analysis that compared running a generative AI workload in the cloud versus on a traditional in-house server setup. The scenario used an open-source LLM (Meta’s Llama 2, a 13B-parameter model) across typical tasks like data processing, fine-tuning, and inference. The results were eye-opening: the AWS and Azure cloud options were estimated to cost up to 2.9× more over three years than the on-premises solution with comparable performance (A cost-benefit analysis of Dell on-premises vs. AWS and Azure  deployments | PT). In fact, even a flexible on-prem option (using a vendor’s pay-per-use model on local hardware) significantly undercut the cloud TCO (total cost of ownership) in that study (A cost-benefit analysis of Dell on-premises vs. AWS and Azure  deployments | PT). While exact figures will vary by use case, the message is clear – for sustained, high-volume AI workloads, owning the means of production (in this case, commodity hardware) can be far more cost-efficient than renting compute time from a cloud provider.

Another aspect is predictable, flat costs. Investing in commodity hardware (standard servers with CPUs/GPUs) is largely a one-time capital expense (plus maintenance and electricity). Once the hardware is in place, an enterprise can run its AI models as often as needed without worrying about per-query fees. This is in contrast to cloud, where each user interaction with an AI model racks up usage charges. As AI becomes embedded in daily business operations (from customer service chatbots to document analysis), those cloud usage fees add up fast. Companies with heavy AI workloads have reported that an upfront hardware investment can pay for itself in as little as 12 months compared to equivalent cloud usage costs (Road to On-Premise LLM Adoption - Part 1: Main Challenges with SaaS LLM Providers - Unit8). After that breakeven, running AI in-house can translate into direct savings year over year.

Of course, organizations must weigh the trade-offs. Running LLMs locally means you’ll need sufficient hardware and the expertise to manage it. There’s an initial expenditure for servers equipped with capable GPUs or high-performance CPUs. You also need IT staff to maintain the systems and update the models as needed. For some smaller businesses or sporadic use cases, the cloud’s pay-as-you-go model might still be more economical. But for many medium and large enterprises using AI every day, the economics increasingly favor an on-prem approach. “A well-timed investment in LLM-capable hardware can pay off quickly, securing modern AI capabilities while letting developers experiment freely without worrying about cloud bills or data liabilities,” as one AI engineering firm observed (Road to On-Premise LLM Adoption - Part 1: Main Challenges with SaaS LLM Providers - Unit8).

In summary, running LLMs on commodity hardware offers cost stability and potential savings that appeal to the CFO’s office as much as the CIO. Companies can avoid cloud sticker shock, budget more predictably, and even re-purpose existing hardware for AI tasks (getting more value from assets they already own). When privacy and compliance benefits are factored in, the ROI case for local AI becomes even stronger.

Performance, Latency, and Reliability

When you run AI models on your own hardware, you’re not just saving money – you can also gain in speed and reliability. For many real-time applications, latency (response time) is critical. If an AI model is hosted in a distant cloud data center, every request has to travel over the internet, which can introduce delays. In contrast, an on-premises deployment sits on the local network, potentially delivering answers in milliseconds. This low-latency advantage is one reason sectors like manufacturing and retail are embracing edge AI. For instance, McDonald’s uses edge computing at its restaurants to process data on-site, reducing latency for AI-driven decisions in kitchen operations (5 ways McDonald's is using AI [Case Study] [2025] - DigitalDefynd). By handling AI tasks locally (sometimes in combination with cloud services), they speed up response times and improve the customer experience. The same principle applies across use cases: whether it’s a chatbot answering customer queries or an analytics model scanning security camera feeds, running the AI nearby eliminates the lag and dependency on internet bandwidth.

Cloud AI providers are aware of rising demand and often need to throttle or queue requests during peak times. If hundreds of companies share a cloud model, performance can fluctuate. A 2024 analysis noted that cloud LLM services sometimes sacrifice latency for throughput as they juggle many users, which “heavily impacts the user’s perceived quality of service” (Road to On-Premise LLM Adoption - Part 1: Main Challenges with SaaS LLM Providers - Unit8). With an on-prem model, your AI service is always available on-demand at full speed, just for you. There’s no contention with other tenants. This dedicated access can be crucial for applications like real-time fraud detection or interactive assistants, where delays could frustrate users or derail a process.

Reliability is another benefit. If your application relies on a cloud API and the provider has an outage or a network glitch occurs, your AI capability could go down. By contrast, an in-house model will keep running as long as your own systems are up. Companies can architect local AI for high availability (with backup servers, etc.), adding resilience to their operations. We’ve even heard of organizations choosing local LLMs to serve remote or field sites – for example, an oil rig or naval ship that can’t depend on continuous internet, or a rural factory with limited connectivity. In such cases, local AI isn’t just a nice-to-have; it’s the only viable option to get AI-assisted insights in real time.

Of course, achieving top performance on commodity hardware may require optimization, which we’ll discuss next. A smaller on-prem setup might not match the raw horsepower of a hyperscale cloud. But thanks to optimization techniques and smart engineering, many businesses find they can get acceptable or even excellent performance from local models. In some specialized scenarios, a well-optimized local cluster can process output faster than a larger generic model running in the cloud (Road to On-Premise LLM Adoption - Part 1: Main Challenges with SaaS LLM Providers - Unit8). Ultimately, controlling your own AI infrastructure gives you the flexibility to tune for the performance that your business needs, when and where you need it.

Customization and Control of Models

Another compelling reason businesses run LLMs on-prem is the unparalleled customization it allows. When using a cloud AI service, you are typically limited to the functionality of the provider’s model. You might get a powerful general model, but it may not speak the jargon of your industry or understand the nuances of your company’s data. On commodity hardware, however, organizations can deploy open-source models and fine-tune them on proprietary data to create a truly custom AI solution. This capability to train or tweak the model in-house is a game-changer for enterprise AI strategy.

Open-source LLMs like Meta’s Llama 2, MosaicML’s models, or EleutherAI’s GPT-Neo family have given companies a foundation to build upon. Businesses can start with a base model and fine-tune it on their domain-specific dataset – all behind their own firewall. For example, a law firm could fine-tune a local LLM on its library of legal documents, making the model an expert in law vocabulary and case precedents. Or a healthcare provider could train an LLM on anonymized medical records to better handle clinical terminology. This level of tailoring would be risky or impossible if done via a public cloud (you wouldn’t upload thousands of sensitive documents to an external server just to fine-tune a model!). On-prem deployment keeps both the training data and the model weights under strict company control.

Customization isn’t just about fine-tuning; it’s also about choosing the right model for the job. Cloud AI offerings often take a one-size-fits-all approach – aiming to be the best at everything from coding help to creative writing. But your business might not need everything. As one expert pointed out, “foundation models like ChatGPT want to excel at a wide range of tasks… For most use cases, many of those capabilities are unnecessary. Why pay for all those extra features and still get subpar performance on the specific task you care about?” (Road to On-Premise LLM Adoption - Part 1: Main Challenges with SaaS LLM Providers - Unit8). By running your own LLM, you can pick a model that is purpose-built for your needs – which might be a smaller, faster model that is just as good (or better) for your particular application. Companies have found that a model tailored to do one thing well can outperform a larger generic model, all while running more efficiently and cheaply (Road to On-Premise LLM Adoption - Part 1: Main Challenges with SaaS LLM Providers - Unit8).

Control extends to how and when the model is updated. With a cloud service, you’re subject to the provider’s update schedule and feature roadmap. In contrast, an internally managed model gives you full control over updates, versioning, and extensions. If you want to integrate a new dataset or implement a safety filter specific to your business, you can do so on your timeline. If the model makes an error, your data science team can analyze it, retrain the model, or adjust the prompt strategy – with complete visibility into the system’s internals. Essentially, you own the entire stack, which can be reassuring for mission-critical applications. This level of control is often required in sectors that demand auditability of AI decisions (for example, being able to explain why an AI-made recommendation to comply with regulations). With a self-hosted model, the “black box” becomes a bit more transparent.

Optimizing LLMs for Commodity Hardware

Running a large language model on commodity hardware is now feasible, but it may require clever optimization techniques to work well. Out of the box, cutting-edge LLMs can be resource-intensive – some have hundreds of billions of parameters, needing dozens of high-end GPUs to run. Most organizations won’t attempt to run a 175-billion-parameter model like GPT-3 from scratch on a single off-the-shelf server. Instead, the key is to optimize either the model or the hardware (or both) to find the right balance of performance and resource use. Fortunately, the AI community has made huge strides in model optimization that make local deployment more practical.

One common approach is quantization, which reduces the precision of the numbers (weights) in the model. By using 8-bit or 4-bit representations instead of the standard 16-bit or 32-bit, models can shrink dramatically in memory size and execute faster – often with only a minor impact on accuracy (A Comprehensive Evaluation of Quantization Strategies for Large Language Models). In fact, researchers have shown that 4-bit precision is surprisingly effective: a recent study noted that a large 65B-parameter model normally requiring ~780 GB of GPU memory could be handled with far less memory using 4-bit techniques, achieving nearly 16× efficiency gains ([PDF] Artificial Intelligence Index Report 2024 - Stanford University). In practice, this means a model that might have needed, say, 8 GPUs could potentially run on just 1-2 GPUs with careful quantization. The trade-off is a slight drop in accuracy or language fluency, but for many enterprise applications the difference is negligible. The bottom line is that quantization lets you run bigger models on smaller hardware.

Another strategy is model distillation or using smaller pretrained models. Distillation involves training a smaller model to mimic a larger model’s behavior, effectively compressing the knowledge. The smaller model is faster and lighter to run, which is ideal for commodity hardware, while still retaining most of the useful capabilities of the big model. Some AI vendors are even releasing “small LLMs” optimized for on-prem use, advertising strong performance on specific tasks with only a fraction of the parameters of their giant cloud counterparts (Small Language Models: A Paradigm Shift in AI for Data Security ...) (Generative AI for utilizing confidential data that is difficult to use on ...). When deploying locally, companies often choose a model size that aligns with their hardware constraints. For example, a 7B or 13B parameter model (which might require 1–2 high-end GPU cards or can even run on a powerful CPU server with enough RAM) might be sufficient to accomplish the task at hand. It’s not always about sheer size – a well-tuned 13B model can outperform a 175B model if tuned to a particular domain.

There are also hardware-specific optimizations. Utilizing GPU acceleration is almost a must for serious LLM workloads on-prem. Luckily, high-performance GPUs (the kind used for gaming or graphics) are a form of commodity hardware these days – widely available and increasingly affordable. Companies are repurposing gaming GPUs or investing in NVIDIA Tensor Core GPUs, which offer excellent AI inference speeds. Software libraries and frameworks (like NVIDIA’s TensorRT, or open-source projects like Hugging Face’s Optimum and DeepSpeed) can further accelerate model inference on given hardware. Meanwhile, for CPU-bound deployments, libraries like INT8/FP16 optimizations or GGML enable surprisingly decent LLM performance without a GPU, by leveraging all CPU vectorization features. The engineering effort to set this up is non-trivial, but many pre-optimized model variants are freely available in the open-source community. Essentially, if latency is not ultra-critical, one can trade a bit of speed for the convenience of running on existing CPU servers.

The takeaway for business leaders is that running AI at the edge is getting easier. Today’s “commodity” hardware is quite powerful – a single server with a couple of GPUs can host a pretty capable language model. Through techniques like quantization and careful tuning, organizations have demonstrated throughput of hundreds of tokens per second from a local LLM cluster (Road to On-Premise LLM Adoption - Part 1: Main Challenges with SaaS LLM Providers - Unit8), which is sufficient for handling many concurrent users or real-time streams. Yes, there are limits – you likely won’t be training a brand-new, state-of-the-art model from scratch on a PC. But for inference (using a model to get answers) and for fine-tuning smaller models, commodity hardware often suffices. Companies need to plan and perhaps get expert help to optimize models, but it’s an investment that pays off in enabling private, fast AI on-site.

Real-World Examples and Use Cases

The move to local AI is not just theoretical – many businesses are already reaping the benefits of deploying LLMs and other AI models on-premises. Here are a few scenarios highlighting how organizations leverage commodity hardware for AI and what they gain:

• Financial Services – Enhanced Privacy and Insight: A global bank handles millions of sensitive documents (contracts, loan applications, client communications). Concerned about confidentiality, they deploy an open-source LLM on their in-house servers to summarize documents and answer bankers’ queries. This internal “AI assistant” helps employees sift through information faster while ensuring no client data ever leaves their data center. The result is improved productivity with zero regulatory headaches about data sharing. (Many banks have banned staff from using public chatbots (Employees are banned from using ChatGPT at these companies), but by hosting their own model, this bank found a compliant way to still leverage AI.) Additionally, the bank fine-tuned the model on financial jargon and its own knowledge base, so the AI provides more relevant answers than a generic model would – a competitive differentiator.

• Healthcare – Compliance and Care Personalization: A hospital network is exploring AI to improve patient care, but strict patient privacy laws mean cloud solutions are off-limits for anything involving personal health information. The hospitals turned to an on-premises AI approach. Using commodity GPU servers, their IT team deployed a medical-focused language model that can assist doctors in writing case notes and drafting treatment reports. Doctors can securely query the model about drug interactions or get a summary of a patient’s history, all without risking a privacy breach. Because the model is local, it can also be directly integrated with the hospital’s electronic health record system – something difficult to do securely with an external cloud API. Early results show doctors spending less time on paperwork and more time with patients, illustrating how local AI can drive efficiency in a highly regulated environment.

• Retail and Manufacturing – Low Latency Edge AI: Imagine a large retail chain with hundreds of stores, or a factory floor with IoT sensors. These environments generate data that often needs immediate analysis (inventory levels, equipment performance, customer behavior in-store). If each location relies on a distant cloud server for AI, the latency and dependence on connectivity could slow things down. Companies like McDonald’s have recognized this and deployed edge AI solutions – in their case, processing data at each restaurant to speed up decision-making (5 ways McDonald's is using AI [Case Study] [2025] - DigitalDefynd) (5 ways McDonald's is using AI [Case Study] [2025] - DigitalDefynd). Similarly, our hypothetical retail chain runs a smaller LLM-based system on a mini-server in each store to assist with real-time restocking decisions and personalized offers to shoppers. The manufacturing firm runs a local predictive maintenance model on-site, flagging anomalies in machine data instantly to prevent downtime. In both cases, commodity hardware (an industrial PC or store-server) is enough to host the AI model. These businesses benefit from ultra-fast responses and continued operation even if the internet connection drops. The local models are also customized to each location’s specific needs (e.g., a store’s AI knows that region’s product catalog and customer preferences deeply). This hyper-local intelligence would be hard to achieve with a one-size-fits-all cloud service.

• Public Sector – Data Sovereignty in Action: Government agencies often handle classified or sensitive information (from legal documents to citizen data) that they simply cannot feed into a third-party cloud model. For example, a national archives department used an on-premises NLP model to digitize and categorize historical documents. By doing it in-house, they ensured that confidential state documents never went to an external server. In another case, a city administration deployed a local language model to power a citizen help chatbot on their municipal website. All interactions are processed on city-owned servers, alleviating concerns about a cloud provider logging the data. These examples show how local AI deployments align with public sector values of data sovereignty and accountability. They get the benefits of AI (improved citizen services, automation of tedious tasks) while upholding strict data governance policies.

These scenarios highlight a common theme: the companies that benefit most from local AI deployments are those for whom data control, quick response, or customization are top priorities. Whether it’s protecting sensitive information, needing instant insights at the edge, or tailoring an AI to speak the language of the business – running LLMs on commodity hardware has proven its value across industries. Each of these examples also underscores that the technology is mature enough to trust in critical workflows. Five years ago, the idea of running an advanced language model on a normal company server might have sounded far-fetched. Today, it’s not only possible – it’s happening.

Comparing Approaches: Cloud AI vs Local AI vs Hybrid

It’s worth noting that choosing between cloud and on-prem AI isn’t an all-or-nothing proposition. Many enterprises adopt a hybrid strategy: they leverage cloud AI for some tasks and local models for others, depending on what makes sense. Let’s briefly contrast the approaches:

• Cloud AI Solutions: These include offerings like OpenAI’s GPT-4 API, Microsoft Azure AI, Google Vertex AI, and countless SaaS products with AI features. The advantages are immediate access to powerful models, no infrastructure to manage, and the ability to scale up or down as needed. Cloud solutions shine for rapid prototyping and when you need cutting-edge capabilities without investing in hardware. However, as we discussed, the downsides are data leaving your control, potential compliance issues, ongoing costs, and less customization. There’s also a degree of vendor lock-in – once your processes rely on a specific cloud model, it can be hard to switch.

• Local AI on Commodity Hardware: This approach uses your organization’s own servers or devices to run AI. The big pros are privacy, control, and potentially lower long-term cost. You can customize models and integrate them deeply with your internal systems. Performance can be optimized for your environment, and there’s no dependency on an internet connection. The challenges include ensuring you have or can acquire the right hardware (GPUs, etc.), and maintaining the system (applying updates, monitoring performance). It also might require hiring or upskilling talent to manage these AI systems. In essence, you trade some upfront investment and complexity in return for autonomy and security.

• Other On-Prem and Hybrid Offerings: It’s interesting to note that cloud providers themselves recognize the demand for on-prem solutions. Tech giants now offer hybrid options like AWS Outposts, Azure Stack, and Google Distributed Cloud, which allow their cloud AI services to run on hardware installed at the customer’s site. These are often turn-key appliances or integrated systems. They attempt to give the “best of both worlds” – cloud-managed AI but with data staying local. For example, Google’s Distributed Cloud helped McDonald’s run AI in thousands of restaurants with centralized management (5 ways McDonald's is using AI [Case Study] [2025] - DigitalDefynd). Similarly, Microsoft and Meta have partnered with enterprise vendors to bring models like Llama 2 into customer data centers easily (Dell and Meta partner to bring Llama 2 open source AI to enterprise | VentureBeat). These solutions can be attractive if a company wants local data processing but doesn’t mind using vendor-specific hardware or software. They do, however, sometimes come at a premium cost (or require existing cloud commitments) and might not offer the full freedom of a DIY commodity hardware setup.

The good news is that the ecosystem is rich – enterprises have choices. A savvy strategy might involve using cloud AI for general-purpose tasks or public-facing applications, while reserving on-prem LLMs for sensitive, high-volume, or highly customized tasks. For instance, a company could use a cloud AI service to power a public website chatbot (where data is mostly non-sensitive FAQs), but use an internal LLM for analyzing proprietary documents. The key is to evaluate each use case on data sensitivity, performance needs, cost profile, and strategic importance. In many cases, businesses are surprised to find that running a capable AI model on readily available hardware more than meets their requirements.

Conclusion: Seizing the Local AI Advantage

The landscape of enterprise AI is evolving. Running large language models on commodity hardware is no longer just a lab experiment – it’s a viable option delivering real business value. Companies that embrace this approach can differentiate themselves with more secure, cost-effective, and tailored AI solutions. They gain peace of mind knowing their sensitive data remains on-premise, and they can innovate faster by fine-tuning models to their unique needs. Perhaps most importantly, they take full ownership of their AI strategy, rather than handing the keys (and a blank check) to a third-party provider.

For forward-thinking business leaders, now is the time to assess how local AI deployments could fit into your broader technology roadmap. Do you have use cases where data can’t leave your site? Are cloud AI bills climbing as usage grows? Do you need an AI model that truly understands your industry or customers? These are telltale signs that running an LLM on your own hardware might be the smart move. With the plethora of open-source models and optimization tools available, the barriers to entry have come down significantly. What used to require a team of PhDs and a supercomputer can often be achieved with a small, agile team and a few good servers.

Next Steps: If you’re considering bringing AI in-house, start with a pilot. Identify a contained project – for example, an internal Q&A assistant or a document summarization tool – and attempt to deploy a smaller-scale model locally. This will help your organization build expertise and confidence with the technology. Engage your IT and data science teams (or external experts) to establish the right infrastructure and practices for managing AI models. And importantly, keep an eye on the rapidly advancing field of AI hardware and software. New breakthroughs in model efficiency and new generations of GPUs/TPUs could further tilt the equation in favor of on-prem solutions.

By harnessing large language models on commodity hardware, enterprises can unlock AI’s potential on their own terms – maintaining privacy, reducing risk, and controlling costs. It’s an exciting time where businesses big and small can finally say, “Yes, we can run that AI ourselves.” Those who do will have a strategic edge in the era of intelligent automation.

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