Bare metal for AI workloads: supporting LLMs and generative AI

Artificial Intelligence is taking over the world. Not as in dystopian realities seen in movies and TV but in terms of AI becoming strategically important. And bare metal server hosting is one of the best ways to enhance yours.

Think about it: large datasets to analyze and learn from, inference, and so many other important aspects of creating reliable AI partners depend on having a powerful and trustworthy digital infrastructure.

By the end of this article, you will understand why dedicated bare metal servers are among the best options for AI workloads if you really want the best results and higher efficiency.

How Does AI Work?

To understand why bare metal servers are particularly suited for supporting artificial intelligence, it’s essential to revisit the fundamentals of AI.

By understanding the basic principles behind this revolutionary technology, we can see how its demands align with the unique capabilities of bare metal infrastructure.

The Three Pillars of AI

AI operates on three foundational pillars: large datasets, complex algorithms, and computing power. Together, these elements form the core of how AI functions, enabling it to learn, process, and deliver insights.

Large Datasets

Datasets serve as the reference material for AI systems, providing the raw information needed to identify patterns and derive insights.

These datasets often consist of millions or even billions of data points, ranging from images and text to audio and video.

The sheer volume of data ensures that the system can account for variability and improve its accuracy over time. For example, large language models (LLMs) like the ones behind ChatGPT rely on diverse datasets containing billions of words to achieve their remarkable fluency.

Complex Algorithms

Algorithms are the "brains" of an AI system. These are mathematical formulas and procedures that analyze data, find patterns, and create models capable of learning and improving.

In essence, algorithms enable AI to move beyond rote memorization and into adaptive behavior, simulating learning in a way that mirrors human cognition, which is why these AI-focused algorithms are called neural networks.

Computing Power

Running algorithms on massive datasets requires immense computational resources.

AI workloads involve millions of operations performed in parallel, such as matrix multiplications in neural networks, which demand high-performance hardware like GPUs, TPUs, or advanced bare metal servers optimized for AI tasks.

Drawing Parallels with Human Learning

If we think about how humans learn, the process is somewhat analogous to AI. The algorithms act as the brain, processing and interpreting the input, while the datasets function as the books or experiences that provide the knowledge base.

However, AI’s "learning" is fundamentally different from human cognition. Humans excel at making intuitive connections, generalizing information, and adapting to unfamiliar situations in ways that AI cannot yet replicate.

Conversely, computers are far faster at performing mathematical calculations and identifying patterns in vast datasets.

Neural Networks: The Backbone of AI

The design of artificial neural networks, particularly in large language models (LLMs) like ChatGPT and Llama, draws inspiration from the structure of the human brain.

These networks consist of layers of interconnected artificial neurons, each responsible for processing specific aspects of the input data.

This layered architecture enables AI to tackle extremely complex problems, from understanding natural language to identifying objects in images.

However, such systems are computationally expensive to train and operate, requiring advanced hardware to function efficiently.

Running an LLM on standard hardware is infeasible; it necessitates infrastructure optimized for high performance, such as bare metal servers with GPU acceleration.

Why Computing Power Matters

At its core, AI uses large datasets to train its models, enabling algorithms to identify patterns and infer relationships.

Training a model like an LLM involves billions of calculations over weeks or months, consuming massive amounts of processing power.

Even after training, inference—using the trained model to generate outputs—requires significant computational resources to deliver results seamlessly.

This dependency on power-intensive processing underscores the importance of robust hardware.

Artificial intelligence, in its most powerful forms, hinges on the interplay of data, algorithms, and computing power.

While datasets and algorithms provide the foundation, it’s computing power that enables AI to achieve its full potential.

Bare metal servers, with their unparalleled performance and ability to handle demanding workloads, are uniquely equipped to support these computational requirements, making them an ideal choice for advancing AI technologies.

The Underlying Infrastructure That Enables AI

End consumers and entrepreneurs alike often think of technology as programs that execute specific tasks or provide entertainment.

It’s easy to focus on the surface—the apps, tools, or devices we interact with daily—while overlooking the critical infrastructure that makes it all possible.

From generative AI models and video games to file organizers, messaging systems, IoT devices, and personal assistants like Alexa, these innovations rely on one thing: servers.

These servers must remain functional at all times to ensure users benefit from what companies offer.

The Foundation of Everything Digital

The digital infrastructure powering technological marvels is often taken for granted. Yet, it is the foundation of everything digital. Without robust servers, the modern technologies we depend on would simply cease to exist.

As mentioned earlier, AI models like ChatGPT or LLaMA require immense computing power for both training and inference.

These processes demand servers equipped with cutting-edge GPUs to deliver the performance and scalability necessary for handling complex calculations.

Online multiplayer games depend on powerful servers to host matches, store player profiles, and ensure synchronized, low-latency gameplay. Even single-player games often connect to servers for updates, cloud saves, and leaderboards.

Services like Google Drive and Dropbox rely on servers to store and manage vast amounts of data, ensuring users can access their files from anywhere.

Similarly, messaging platforms like WhatsApp and Slack use servers to route billions of messages daily, manage user data, and store chat histories securely.

Self-driving cars, while performing much of their computations locally, rely on servers for training the AI, aggregating driving data, and delivering real-time updates.

Personal assistants like Alexa also rely heavily on servers to process voice commands, retrieve information, and learn user preferences.

Servers: The Backbone of Modern Business

Servers are ubiquitous. If there’s an internet connection, there are servers working behind the scenes. For businesses, particularly those leveraging AI, choosing the right server infrastructure is critical.

AI workloads bring unique demands that make servers a more delicate and essential consideration.

For example, running large-scale AI models depends on having enough computational power to handle inference tasks efficiently.

Without the right infrastructure, these processes slow down, delaying insights and making the AI less useful in real-world applications.

Security and Precision: Non-Negotiable for AI

The importance of servers goes beyond performance. Security is paramount.

Companies handling sensitive data—from proprietary algorithms to confidential customer information—cannot risk breaches. Servers must not only process workloads efficiently but also protect data from malicious actors.

In addition, AI workloads require exceptional precision. Models must read datasets accurately, make calculations quickly, and produce reliable outputs.

There is no shortcut. Whether training a model or deploying it for real-world use, every aspect of the server’s performance impacts the final result.

Bare Metal Servers: Essential for AI

Eventually, every business leveraging AI will encounter infrastructure bottlenecks.

When that happens, it becomes clear that a high-performance bare metal server can actually meet the demands of large language models (LLMs) and other AI workloads.

Bare metal servers provide the raw power and reliability necessary for AI applications, enabling fast inference, robust training, and secure data handling.

Businesses working with AI must partner with providers capable of delivering the compute power, security, and precision required for success.

Without the right infrastructure, AI ambitions remain just that—ambitions. But with the proper servers, businesses can turn AI into a powerful, scalable, and transformative tool.

How to choose the best bare metal provider

Selecting the best bare metal provider for your workload isn’t a one-size-fits-all process.

The key lies in understanding your specific requirements and finding a provider capable of addressing them effectively.

That said, some universal qualities should guide your choice of a bare metal provider.

While they might seem obvious, it’s easy to overlook them—sometimes with costly consequences.

Customer Reputation

Start by examining what existing customers say about the provider. Who have they worked with, and what kind of feedback have they received?

A trustworthy provider will showcase customer testimonials, case studies, or success stories.

If no such information is available, it could indicate they’re not delivering consistent value to their clients.

Hardware Flexibility

Another crucial factor is the hardware offering. Does the provider offer customizable configurations tailored to your workload?

High-quality providers will accommodate specific needs, ensuring that you get the right balance of compute power, storage, and networking.

For example, providers like Latitude.sh are known for their ability to deliver highly tailored solutions through a responsive sales team.

Cost-Effectiveness

Compare the costs of bare metal and public cloud offerings. Public cloud pricing can often seem competitive, but are you getting sufficient compute power for the price?

In many cases, opting for private cloud or bare metal servers delivers far superior performance for a similar cost.

Understanding your workload’s demands and matching them to the most cost-effective solution is critical.

Key Components to Look For

Bare metal servers often come equipped with advanced components like NVMe drives, DDR5 memory, powerful GPUs, and robust network interface options.

These specifications directly impact performance and should not be overlooked. Evaluate whether the provider’s offering aligns with your performance needs, especially for workloads like AI, high-performance computing, or gaming.

AI workloads with Latitude.sh

Bare metal servers come in a variety of configurations to meet different workload needs. Latitude.sh is ready to get you what you need, as you can see below.

If you’re looking for a more entry-level option for AI inference workloads, a server like the g3.h100.small might be ideal, offering a single NVIDIA H100 80GB GPU, 2 x 3.8TB NVMe storage, and 20 TB of free monthly egress for $1,949 per month or $2.67 per hour.

If high GPU density is a priority, the g3.l40s.large offers eight NVIDIA L40s GPUs, 4 x 3.8TB NVMe storage, and 20 TB of free monthly egress for $3,679 per month or $5.04 per hour, making it one of the most cost effective instances for training small to medium-sized models, fine-tuning open-source LLMs and running inference.

For workloads requiring even greater GPU performance, the g3.a100.large features eight NVIDIA A100 GPUs with NVLink, 4 x 3.8TB NVMe storage, and 20 TB of free monthly egress for $7,975 per month or $10.92 per hour.

Finally, the g3.h100.large offers unmatched performance for its generation, with eight NVIDIA H100 GPUs in NVLink, 4 x 3.8TB NVMe storage, and 20 TB of free monthly egress, priced at $12,441 per month or $17.04 per hour.

These examples illustrate the flexibility available with bare metal servers, ensuring that businesses can select a configuration that perfectly matches their specific performance and budget requirements for different AI worklaods.

AI goes beyond LLMs and will keep growing

While large language models (LLMs) like ChatGPT and Gemini demonstrate remarkable general-purpose capabilities, the scope of AI extends far beyond what these models can achieve.

Many organizations should (and will) continue to invest in developing their own AI systems, and the reasons are clear. Specialized AI models are essential for niche applications that require precision and focus.

Whether it’s analyzing medical imaging, optimizing supply chains, or delivering hyper-personalized recommendations, tailored models outperform general-purpose ones by addressing specific challenges directly.

Another critical factor is control over data and privacy. Industries like healthcare, finance, and defense handle sensitive information that must remain secure and compliant with strict regulations.

In-house AI development ensures proprietary data stays protected and accessible only within trusted environments.

Cost efficiency is also a major driver. For businesses with large-scale AI workloads, training and deploying custom models on dedicated infrastructure can be more economical than paying for ongoing access to third-party APIs.

Similarly, custom-built models offer unparalleled flexibility, allowing organizations to fine-tune performance, latency, or energy consumption to fit their exact needs.

Moreover, proprietary AI solutions provide a competitive edge. Businesses gain unique capabilities unavailable to competitors using off-the-shelf models, securing their position in the market.

Ownership of these AI technologies also represents a valuable intellectual asset, which can lead to licensing opportunities or product integrations.

AI innovation isn’t limited to the bounds of LLMs—it’s a dynamic and ever-evolving field.

Organizations that invest in building their own models create a foundation for experimentation and growth. Whether adapting to new data types, solving emerging problems, or exploring uncharted territories in AI, this flexibility fuels continuous advancement.

As technology progresses, the role of AI will only expand, encompassing applications we’ve yet to imagine. By going beyond general-purpose LLMs, businesses and researchers can harness the transformative power of AI to innovate and thrive in a rapidly changing world.

Join Latitude.sh today and leverage the best solution for your AI workloads.