Distributed training addresses compute scarcity in AI by distributing workloads across multiple devices, enabling efficient scaling, faster training, and cost-effective resource management.
It looks a lot like a distributed future. As AI models become more advanced, more specialized, larger, single GPUs aren’t enough. You can do it, but it can take weeks or months, which is unsustainable in the long run. Instead, distributed training could just be the answer.
Is compute becoming more scarce?
Distributed training for AI models involves splitting the workload of training a model across multiple devices, processors, or machines to accelerate the process and handle larger datasets. This method is crucial for training deep learning models, particularly when dealing with large-scale models or enormous datasets that a single machine cannot efficiently process.
Types of distributed training include:
- Data Parallelism: In this approach, the model is copied across multiple devices (GPUs or machines), and each device processes a different subset of the data. After each training step, the results (i.e., gradients) are synchronized and averaged to update the model.
- Model Parallelism: Here, the model itself is split across multiple devices. Each device handles a different part of the model, processing the full input or parts of it. This is helpful for extremely large models that can’t fit into the memory of a single GPU or device.
- Pipeline Parallelism: A variation of model parallelism where different stages of the model are placed on different devices. Inputs flow through the devices like an assembly line, allowing for better efficiency in training deep models.
- Hybrid Parallelism: A combination of data and model parallelism, used when both the model is too large for a single device and the dataset is too large for a single pass on one machine.
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Why split the workload?
Splitting the workload through distributed training allows AI models to scale, process more data, and achieve faster training times.
Training Larger Models
Modern AI models, such as large language models (LLMs) like GPT-4 or advanced computer vision models, have billions of parameters. These models often exceed the memory capacity of a single device. By distributing the model itself across several machines (model parallelism), it’s possible to train huge models without hitting hardware limitations.
Reducing Training Time
Training large AI models can take days, weeks, or even months on a single machine. Splitting the workload across multiple devices (e.g., GPUs or machines in a cluster) reduces the training time significantly. This is critical in research and industry environments where faster iteration cycles are necessary to improve models, tune hyperparameters, and deploy solutions.
Overcoming Hardware Limitations
Modern models, particularly deep learning models, are often constrained by hardware limitations like GPU memory. For example, a single GPU might not have enough memory to process a large batch size or complex model architecture. Distributed training can split both the model and the data across multiple GPUs, allowing the training process to scale without running into these memory bottlenecks.
Better Utilization of Resources
Organizations with access to cloud resources or on-premises clusters often want to make the best use of these resources. Distributed training allows them to spread the computational load across many available machines, balancing the workload and maximizing the overall throughput of the system.
See also: How AI is Driving the Shift to a Private Cloud
Why are compute resources becoming more scarce?
Compute resources are not necessarily becoming scarcer, per se. It’s that the demand for compute is growing exponentially, especially with the rapid advancement of AI, machine learning, and other data-intensive technologies. Several factors contribute to this perception of scarcity, even as hardware capabilities and cloud infrastructure continue to improve.
Growth of AI and Deep Learning
Why it feels scarce: The scale of these models can outpace the availability of high-end GPUs or TPUs, particularly when demand spikes (e.g., during training for AI advancements).
AI models, especially deep learning models, have been growing in size and complexity. Large language models like GPT-4, GPT-5, and others require massive compute power for both training and inference. The amount of data and computational power needed to train these models is immense, straining even cutting-edge hardware.
Specialized hardware and GPU shortages
Why it feels scarce: Even though general-purpose compute (like CPUs) is widely available, specialized compute for high-performance tasks, particularly in AI, can be harder to access due to high demand and constrained supply.
Specialized hardware like GPUs and TPUs, which are essential for AI and machine learning, have experienced shortages. This scarcity was exacerbated by the pandemic, which caused supply chain disruptions, and by the high demand from industries like cryptocurrency mining, which also uses GPUs.
Energy and cost constraints
Why it feels scarce: Financial constraints may make it difficult for companies to deploy the amount of compute they need, giving the impression of scarcity. Even in the event of big budgets, aging infrastructure makes operational costs high.
High-performance computing (HPC) and data centers require substantial amounts of energy, leading to high operational costs. As energy prices rise, maintaining large compute infrastructures can become more expensive. For companies, the financial cost of acquiring and maintaining sufficient compute power might create the sense of compute becoming “scarcer” because it’s cost-prohibitive to scale resources at the required rate.
Complexity of optimizing compute efficiency
Why it feels scarce: Even though resources might technically be available, suboptimal utilization can lead to unnecessary strain on compute capacity.
Achieving maximum efficiency in utilizing compute resources is complex. Many organizations are not fully optimizing their infrastructure for AI, often resulting in wasted resources or inefficient scaling. Effective distributed training, for instance, requires optimizing resource allocation across multiple nodes, which not all companies are well-equipped to manage.
Sustainability concerns
Why it feels scarce: Efforts to reduce the environmental impact of AI and related infrastructure can make expanding compute infrastructure slower and more expensive, contributing to a feeling of scarcity.
As the need for compute increases, so do concerns about sustainability. Large-scale compute, particularly in data centers and cloud environments, consumes significant energy. The environmental impact is pushing organizations to rethink how much compute power they use and how they can make it more efficient or sustainable.
Tackling the growing demands of our AI
While compute itself is not becoming scarcer—thanks to ongoing scaling by cloud providers and hardware manufacturers—the rapid growth in demand, especially in AI, makes it feel increasingly constrained. Distributed training addresses this by spreading workloads across multiple devices, helping companies maximize infrastructure, reduce training time, and scale AI cost-effectively.
Though compute resources are decentralized in this approach, central coordination remains key to orchestrating these processes. Distributed training enables organizations to tackle larger, more complex AI projects efficiently, balancing the demands of scale with resource limitations to stay competitive in what continues to be a swiftly changing field.
