Table of contents
The evolution of AI model scalingWhy GPUs are still the best option for training and inferenceTrain, rent, or outsource: what’s the right move?Real-world scaling challenges: supply, cooling, interconnectsSecure, scalable infrastructure for enterprise AINext stepsAdditional resources
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GPU → AI Model Scaling

AI model scaling and the GPU race: What enterprises need to know

The scale of today’s AI models is exploding, and with it comes a growing demand for serious GPU infrastructure. Enterprises building large language models, generative AI systems, or real-time inferencing pipelines are hitting the same wall: limited access to powerful GPUs. That’s fueling a global GPU arms race—and it’s not just the tech giants scrambling for computing power.

If your business is scaling AI workloads, it’s time to take a closer look at how dedicated GPU servers can give you the upper hand.

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The evolution of AI model scaling

Model scale isn’t just hype—it’s strategy. Over the past few years, transformer-based architectures have rewritten the rules for what AI can do, especially in natural language processing, computer vision, and multi-modal fusion.

Enterprises are now fine-tuning foundation models, running multi-billion parameter inference pipelines, and deploying AI at the edge. But as context windows expand and model complexity grows, latency and throughput requirements push past what legacy compute can handle. 

You need hardware that can keep up.

Why GPUs are still the best option for training and inference

Modern AI workloads—especially training large language models and running high-throughput inference—demand massive parallel processing power and high memory bandwidth. That’s where enterprise-grade GPUs come in.

The L40S excels at large-scale inference, generative AI workloads, and multimodal applications that integrate language, vision, or audio. With 48GB of GDDR6 memory and over 90 TFLOPS of FP16 compute, it can handle complex real-time inferencing tasks while still supporting visualization and rendering when needed. 

The L40S is particularly well-suited for production environments where throughput and performance consistency are critical.

The NVIDIA H100 94GB represents the next generation of accelerated computing for AI. Built on the Hopper architecture, it delivers dramatic improvements in training speed, memory bandwidth, and transformer engine performance. 

With up to 1.5 TB/s memory bandwidth and support for FP8 precision, the H100 excels in massive-scale model training and advanced AI workloads like retrieval-augmented generation and scientific computing. 

For enterprises needing even more power, systems with dual H100 GPUs offer NVLink connectivity for tightly coupled training across GPUs—ideal for multi-trillion parameter models or demanding multi-node orchestration.

Train, rent, or outsource: what’s the right move?

As AI models scale past billions of parameters, the infrastructure decisions behind them become just as strategic as the models themselves. Enterprises generally face three paths when it comes to accessing GPU power: own it, rent it, or outsource it entirely.

Owning GPU servers offers full control and maximum performance consistency. It’s ideal for teams running sensitive or long-duration workloads where data sovereignty or compliance is a concern. 

But the upfront CapEx, long procurement cycles, and ongoing maintenance demands can be a barrier, especially when hardware refreshes every 18–24 months.

Renting dedicated GPU servers delivers bare metal performance without the hardware investment. This approach gives enterprises fixed monthly pricing, full OS access, and the ability to scale vertically with high-memory configurations. 

Because it’s not virtualized, there’s no resource contention—making it well-suited for training, fine-tuning, and real-time inference under predictable workloads.

Outsourcing to GPU-as-a-Service platforms provides an easy on-ramp to AI compute via APIs or managed runtimes. These services are great for one-off training jobs, batch inference, or experimentation. 

However, GPUaaService often limits flexibility, requires adapting to specific SDKs, and can become cost-prohibitive at scale—especially if model weights, datasets, or tuning scripts need to be updated frequently.

Real-world scaling challenges: supply, cooling, interconnects

Even the most sophisticated AI teams hit infrastructure limits fast. Among the biggest challenges:

  • Hardware availability: GPUs like the H100 and L40S are in short supply worldwide. With a GPU hosting provider, you can reserve these GPUs in advance—no waiting on cloud quotas or spot queues.
  • Thermal and power constraints: Providers’ data centers are optimized for high-density GPU workloads, with redundant power and advanced cooling to keep performance consistent.
  • Bandwidth bottlenecks: PCIe 4.0 and NVLink support in our GPU servers helps reduce latency between GPUs and CPUs, which is critical for high-speed training runs and concurrent inference tasks.

Scaling with multi-GPU setups

When one GPU isn’t enough, Liquid Web lets you scale out with multi-GPU configurations on bare metal. You can run:

  • Data parallelism to split massive datasets across GPUs
  • Model parallelism for breaking up large model layers
  • Multi-instance GPU (MIG) features on the A100 for isolated, parallel inference jobs

Because these servers are dedicated to your workload, you get predictable throughput and full environment control—without fighting for resources in a noisy cloud.

Secure, scalable infrastructure for enterprise AI

Single-GPU systems can handle a surprising amount of inference and fine-tuning, but when you’re working with multi-billion parameter models or running large training jobs, one GPU usually isn’t enough. Multi-GPU setups provide a clear path to scaling up AI workloads without moving to a full cluster.

There are several strategies for distributing compute across multiple GPUs:

  • Data parallelism: Each GPU gets a slice of the training data and maintains its own copy of the model. Gradients are aggregated and synchronized after each training step. This is the most common and easiest to implement approach using frameworks like PyTorch DDP or Horovod.
  • Model parallelism: Large models are split across GPUs by partitioning layers or tensor blocks. This allows you to train models that don’t fit into a single GPU’s memory, but it requires more custom implementation and comes with higher inter-GPU communication overhead.
  • Pipeline parallelism: Layers of the model are organized into stages and assigned to different GPUs. Each mini-batch flows through the pipeline, keeping all GPUs busy. This approach improves utilization but introduces pipeline latency.
  • Multi-instance GPU (MIG): On GPUs like the A100, a single physical GPU can be split into several isolated instances. This is ideal for serving multiple inference workloads or running concurrent jobs without resource contention.

Whether you’re training from scratch, fine-tuning LLMs, or running hundreds of simultaneous inference threads, multi-GPU servers give teams the flexibility to scale horizontally without managing a full-blown distributed cluster.

Additional resources

What is a GPU? →

A complete beginner’s guide to GPUs and GPU hosting

Best GPU server hosting [2025] →

Top 4 GPU hosting providers side-by-side so you can decide which is best for you

A100 vs H100 vs L40S →

A simple side-by-side comparison of different NVIDIA GPUs and how to decide

Amy Moruzzi is a Systems Engineer at Liquid Web with years of experience maintaining large fleets of servers in a wide variety of areas—including system management, deployment, maintenance, clustering, virtualization, and application level support. She specializes in Linux, but has experience working across the entire stack. Amy also enjoys creating software and tools to automate processes and make customers’ lives easier.