Let’s face it: Large transformer models are pretty much the superheroes of the AI world right now. They power some of the coolest applications we see around us—conversational agents that can chat with you on just about any topic, recommendation systems that know what you’ll love watching next, and language models that can write everything from poems to code. But the secret behind these giant models—the ones with billions or even trillions of parameters—isn’t just clever math and piles of GPUs. It’s also about how we move massive amounts of data around. In other words, if your model is Superman, your network is like the Flash, zooming data back and forth at lightning speed.
In this article, we’re going to chat about how training these large transformer models works, why parallelism (splitting the work across multiple GPUs or servers) is so crucial, and how advanced networking technologies like RDMA over Converged Ethernet (RoCE) v2 and InfiniBand step in to save the day. We’ll try to keep it as friendly and relatable as possible, so think of those GPUs as your cooking team, your data as the ingredients, and your network as the conveyor belt that ferries those ingredients to all the chefs at just the right time.
The Challenge of Large Transformer Models
Before we dive into the networking part, let’s set the stage. Transformer models have gotten enormous—so big that we’re past the point where a single GPU, no matter how mighty, can handle all the computations and memory requirements. Sure, a single modern GPU is a beast, but when you’re training a multi-billion parameter language model, you’re dealing with an astronomical number of weights (parameters) that need constant updating through backpropagation. The sheer scale of these models often outstrips the capacity of one GPU’s memory and compute power.
To tackle this problem, we break the training job into pieces and spread them out across multiple GPUs and machines. There are several strategies to make this happen, with data parallelism and model parallelism being two of the big ones.
Both forms of parallelism can even be mixed. You might have a hybrid scenario: Some GPUs split the data while others split chunks of the model. It’s like scaling up a restaurant kitchen with multiple teams of specialized chefs, each with their own ingredients and recipes, working in perfect harmony.
Why Networking Matters
Now here’s the kicker: No matter how you parallelize, you’re going to have a ton of communication between GPUs. If you’re doing data parallelism, after each batch of training data, all GPUs need to share their model parameter gradients with each other. For model parallelism, different parts of the model (split across GPUs) need to send activation maps and gradients back and forth during the forward and backward passes. This communication must be fast, reliable, and efficient. Otherwise, all those powerful GPUs will just be waiting around, twiddling their thumbs (or GPU cores), because the data or parameters they need aren’t arriving quickly enough.
Think of it this way: Having the world’s best chefs doesn’t help if the ingredients move from storage to the kitchen at a snail’s pace. Even if each chef can cook lightning fast, the whole operation grinds to a halt if they’re constantly waiting for ingredients or instructions from others. High-performance networking is that super-fast conveyor belt that ensures everyone gets what they need, exactly when they need it.
RoCE v2 and InfiniBand
When it comes to high-speed networking in large-scale AI training clusters, two technologies often come up: RoCE v2 and InfiniBand. Both are all about reducing latency and increasing bandwidth to ensure that data can move quickly between GPUs or nodes in a cluster.
Both InfiniBand and RoCE v2 significantly reduce latency (think: the delay between asking for ingredients and receiving them) and improve bandwidth (think: how many ingredients you can move at once). For large transformer training, where you might be scaling across dozens, hundreds, or even thousands of GPUs, these network improvements are not just nice-to-have extras—they’re critical.
Parallelisms and the Need for Speed
When training a massive transformer model, it’s not unusual to have hundreds of GPUs working together. Let’s take a scenario and highlight why we need this networking oomph.
Data parallelism example: Suppose you have 256 GPUs across multiple servers with each GPU crunching through a different subset of your training data. After each mini-batch, every GPU will have a set of gradients that represent how the model’s weights should change. Now, if all GPUs need to share these gradients with each other, that means a massive all-to-all communication. Without a high-speed, low-latency network, this step becomes a huge bottleneck. If it takes too long to share the gradients, your GPUs end up idle, waiting. With RoCE v2 or InfiniBand, this gradient exchange can happen blazingly fast, thereby keeping GPUs busy doing what they do best—crunching numbers—instead of waiting around.
Model parallelism example: Consider you have a single, incredibly large transformer model that’s split into four “slices” of the network, each hosted by different GPUs. To do a forward pass on a single input, you start at the first slice, compute the activations, send those to the next slice, and so on. During the backward pass, gradients flow in the reverse direction. If each transfer between slices is slow, your entire training step is delayed. InfiniBand and RoCE v2 come into play here by making those activations and gradients zip across GPUs in microseconds rather than milliseconds. It’s like having a high-speed rail line between different parts of a big factory, which ensures that partly assembled products move instantly from one workstation to the next.
Pipeline parallelism (a variation on model parallelism): Let’s also touch on pipeline parallelism, another strategy where we split the model into segments and line them up like a pipeline. Each segment processes a batch of data in sequence, passing partial results along. The faster these partial results can move between pipeline stages, the more efficient the pipeline. Think of it like an assembly line: If the conveyor belt is slow, everyone ends up waiting. High-performance networking acts as the world’s fastest conveyor belt, keeping the pipeline humming at full speed.
A World Beyond One Machine
In the early days of deep learning, you could fit a decent-sized model on a single GPU and just let it run. As models grew, you might have spread training across a few GPUs on one machine, connected by a fast NVLink or PCIe bus. But now, with giant transformer models, you’re scaling across entire clusters of machines, each machine with multiple GPUs. Your network becomes the nervous system of this giant computing organism. InfiniBand and RoCE v2 ensure that the signals (your data and gradients) travel quickly and reliably across this massive cluster.
Choosing Your Networking Technology
So, which do you pick—InfiniBand or RoCE v2? It often depends on your existing infrastructure and specific requirements. If you’re setting up a specialized high-performance computing (HPC) environment from scratch and need the absolute lowest latency and highest performance, InfiniBand is a strong choice. On the other hand, if you’re working in a more mainstream data center environment that’s built around Ethernet, RoCE v2 lets you achieve HPC-like performance without a wholesale redesign. Many cloud providers and AI hardware vendors support these technologies, which makes it easier to adopt them for large-scale training workloads.
The Bottom Line
At the end of the day, training massive transformer models is a team sport. You’ve got dozens or hundreds of GPUs working in parallel, slicing up the workload so that it’s actually possible to train these behemoth models in a reasonable time. But all that parallelism comes with a need: super-fast, low-latency communication. That’s where RoCE v2 and InfiniBand step in. They make sure that when one GPU says, “Hey, I’ve got these gradients, who needs them?” the response isn’t a cricket-chirping silence or a slow trickle of data. Instead, it’s an instantaneous flood of information shooting back and forth, letting your training process run at top speed. In other words, large-scale transformer training is like a giant professional kitchen. Your GPUs are the master chefs, your data is the constant stream of ingredients, and your model architecture defines how those chefs cooperate and share responsibilities. But without a fast conveyor belt (network) delivering ingredients and instructions where and when they’re needed, the whole operation falls apart. RoCE v2 and InfiniBand ensure your “kitchen” stays efficient, thereby letting you serve up the biggest, most impressive AI models faster than ever before
