Scaling AI: The Essentials of Distributed Machine Learning

Discover how distributed machine learning powers today’s largest AI models like GPT-4 and Stable Diffusion. Learn foundational parallel computing concepts, explore real-world strategies, and understand when and how to effectively apply distributed ML techniques.

When OpenAI released GPT-3 with 175 billion parameters, it broke records. Now, Meta’s Llama 3.1 contains 405 billion parameters, NVIDIA’s and Microsoft’s Megatron-Turing NLG holds 530 billion, and experts believe GPT-4 exceeds one trillion parameters!

Let’s grasp what this truly means: each parameter typically needs 4 bytes of storage in 32-bit precision, so GPT-3 alone requires about 700 GB of memory just to store its parameters. But training demands even more – we need memory for gradients and optimizer states, pushing requirements well beyond 1 TB for GPT-3. Yet NVIDIA’s powerful H100 GPU offers only 80 GB of memory. The gap is enormous. No single GPU can possibly handle these models. So how do researchers and companies train, deploy and serve these colossal models? The answer lies in distributed machine learning – the backbone concept powering both the creation and operation of today’s AI revolution.

In this article, I will provide a gentle introduction to Parallel Computing and Distributed Machine Learning. In upcoming articles in this series, we’ll dive deeper into foundational computing concepts, distributed computing frameworks, key algorithms, architectural best practices, optimization techniques, and real-world case studies from industry/academic leaders.

Distributed Machine Learning: Not Born Overnight

Distributed ML builds on decades of ideas from parallel and distributed computing. Let’s start with what parallelism actually means and why it matters for AI!

Imagine you need to brighten a massive 100-million-pixel image. If you processed each pixel sequentially, one after another, you’d waste precious time. Instead, a better approach would be to divide this huge image into smaller subregions and process them simultaneously across multiple CPU cores or GPU threads. This is data parallelism, where we run the same operation simultaneously on different chunks of data. Data parallelism powers modern GPUs. They excel at processing thousands of operations at once using parallel execution units called GPU kernels. This design perfectly matches common machine learning tasks like matrix multiplications and convolutions.

But parallelism doesn’t stop with data. There’s also task parallelism, where different computing units run entirely different tasks simultaneously. For example, imagine a video game running on your computer. One CPU core might handle physics simulations, computing how objects move and interact in real time. At the same time, a different CPU core could handle rendering tasks, converting the game’s virtual 3D models into the pixels displayed on your screen. Although common in general computing, task parallelism isn’t typically how deep neural network training is structured, because neural network computations usually involve identical operations performed on massive data batches.

Parallel Computing Meets ML: Scaling Models and Data Efficiently

So, how does parallel computing relate to these massive GPT models? It turns out these models aren’t just huge because of their parameters, the amount of data they use for training is equally staggering.

Today’s frontier models are multi-modal, massive in size, and learn from datasets spanning hundreds of billions of words or billions of image-text pairs. Processing this gigantic amount of data sequentially on a single GPU could take years, making progress impossibly slow. But parallel computing offers a practical solution: we split the workload across many GPUs, letting us train these huge models in weeks rather than years.

There are two main ways researchers and ML practitioners use parallelism for machine learning: data parallelism and model parallelism. Often, they blend both methods, creating what we call hybrid parallelism.

Data Parallelism: Tackling Huge Datasets

In pure data parallelism, each GPU holds a complete copy of the same model, but each processes different chunks of data simultaneously. After finishing each step, GPUs synchronize by exchanging and aggregating their results, usually gradients calculated from their respective data subsets. Although powerful, this need for synchronization brings complexity. GPUs constantly communicate, which can slow things down if not carefully managed. We’ll address this topic in detail in the subsequent articles.

Let’s look at a real-world example: Stable Diffusion. The first version of Stable Diffusion had around one billion parameters, which is far smaller than GPT-3. But Stability AI still needed massive computing resources to train it. Why? Because the dataset was enormous, containing billions of image-text pairs. Stability AI trained Stable Diffusion v1 on 256 NVIDIA A100 GPUs for more than three weeks. That is around 150,000 GPU-hours, costing roughly $600,000 in cloud compute costs.

Stable Diffusion clearly shows that even models with fewer parameters can still require extensive distributed setups if the data is massive.

Model Parallelism: Splitting Huge Models

What if the model itself is too large to fit onto one GPU? That’s when model parallelism comes into play.

In model parallelism, the huge model is split across multiple GPUs. Imagine GPT-3 or an larger model: it’s too large to hold entirely on a single GPU. Instead, we divide it into segments and there are 2 ways to do it.

Pipeline Parallelism

In pipeline parallelism, the model layers are divided sequentially across multiple GPUs. Imagine a neural network with 30 layers. Instead of placing all layers on a single GPU, which would be impossible for very large models, you spread them out. GPU 1 handles layers 1 through 10, GPU 2 processes layers 11 through 20, GPU 3 manages layers 21 through 30, and so forth.

As data enters the model, it moves step-by-step from one GPU to the next. Each GPU processes its assigned layers, and then passes its output forward to the next GPU, much like an industrial assembly pipeline!

Pipeline parallelism can introduce waiting time, especially if the compute loads of the GPUs are unequal. If GPU 2 is still busy processing its data, GPU 1 will have to wait before sending the next batch. This waiting time is directly related to a concept known as Amdahl’s Law, which states that the overall speedup from parallelizing a task is limited by how much of the task must remain sequential. In this case, if one GPU becomes slower (perhaps it’s processing a computationally heavier layer), all other GPUs must wait. This sequential bottleneck reduces overall efficiency, demonstrating Amdahl’s Law in action. To address this, we need to carefully balance workloads across GPUs, minimizing bottlenecks and communication delays.

Tensor Parallelism

Sometimes even a single layer within a model, like the attention layer of a transformer, can be too large to fit comfortably on one GPU. This is where tensor parallelism comes into play. Instead of assigning whole layers to separate GPUs, tensor parallelism splits the calculations within a single large layer across multiple GPUs.

For example, imagine a huge transformer attention layer that involves multiplying very large matrices. Rather than computing the entire matrix multiplication on one GPU, the calculation is broken into smaller chunks. Each GPU computes part of the operation simultaneously. Afterward, the GPUs exchange their intermediate results to combine them into the final output.

This approach effectively reduces memory requirements on each GPU. But it comes with increased complexity. Since each GPU computes only part of the result, they must frequently communicate and synchronize their outputs with each other. This communication can slow down the training if not managed carefully.

Hybrid Parallelism: Best of Both Worlds

We have seen that data parallelism helps when datasets become huge, and model parallelism solves the problem when models are too large. But what if you face both challenges at once? Today’s frontier models like GPT-4 or Llama 3.x will run into exactly this situation. They have massive datasets and colossal parameter sizes. To handle both efficiently, we have to blend data and model parallelism into what we call hybrid parallelism.

In hybrid parallelism, you first split the enormous model across multiple GPUs, using either pipeline or tensor parallelism (model parallelism). Then, instead of using just one copy of this split model, you create several copies. Each set of GPUs handles a copy of the split model, and each copy processes a different subset of the dataset simultaneously (data parallelism).

Hybrid parallelism allows us to effectively scale both model size and data volume simultaneously, dramatically reducing training times. However, it multiplies complexity. Synchronization happens both within each GPU set (model parallelism) and across sets of GPUs (data parallelism), significantly increasing communication overhead.

Distributed ML: Powerful, But Not Always the Answer

Although distributed training is extremely powerful, and often unavoidable, it brings significant challenges. These include increased communication overhead, difficulties in debugging that require advanced monitoring tools, high costs, and most importantly, the reality that it may not actually be necessary.

Distributed machine learning makes sense when your models and datasets clearly surpass single-device limits. Frontier models like GPT-4, Llama 3.1, or Megatron-Turing require distributed ML due to their massive scale, both in terms of parameters and training data. Similarly, advanced computer vision tasks such as 3D reconstruction (NeRF, Gaussian Splatting) and generative AI models (Stable Diffusion) greatly benefit from distributed approaches, particularly during training.

However, if your task and resources permit simpler solutions, explore those first! Could single-GPU training, fine-tuning with LoRA, quantization, or vertical scaling be sufficient? Do you genuinely require massive amounts of training data for your model? Distributed ML is incredibly powerful but it’s best used strategically and thoughtfully, rather than automatically!

What’s Next in This Series?

In this article, I introduced the core concepts of distributed machine learning, starting from foundational ideas in parallel computing and then exploring how these apply specifically to modern AI/ML models. In future articles, I will dive deeper into:

In upcoming articles, I will dive deeper into:

• Fundamental parallel computing concepts, such as SIMD architectures, threading, and GPU kernels.
• Communication frameworks like NVIDIA’s NCCL and the MPI protocol.
• Distributed ML frameworks, including DeepSpeed, PyTorch Distributed, TensorFlow Distributed Strategies, and emerging tools like Alpa and Colossal-AI.
• Optimization methods crucial for distributed ML, including Zero Redundancy Optimizer (ZeRO), Fully Sharded Data Parallelism (FSDP), and gradient compression techniques.
• Detailed architectural studies of real-world distributed machine learning systems from leading research labs and industry experts.

Related Reading

Enjoyed this article? To further build your foundational knowledge, check out my previous articles on how software engineers and tech leaders can effectively upskill in AI/ML! My ongoing series of deep-dive articles is designed specifically to help engineering and research leaders evolve their skill sets, balance technical expertise with leadership responsibilities, and stay ahead in a rapidly changing technical landscape:

• The New Technical Leadership: Embracing AI/ML as a Core Competency: Discover how tech leaders can transition to AI/ML, build foundational skills, grow with their teams, and embrace real-world applications for lasting impact.
• AI for Software Engineers: Evolve, Don’t Restart: Explore how software engineers can evolve into AI/ML roles, leveraging existing skills with practical insights and guidance.

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