Supercharge AI model training with DigitalOcean GPUs
Model training workloads require the right computing power to quickly complete calculations, lower inference, and support real-time AI use cases like generative music composition and fraud detection. With GPUs, you can meet all these goals and train your models for the most accurate predictions and answers.
Integrate data, train models with on-demand GPUs
DigitalOcean offers a wide portfolio of GPU configurations, available GPUs from AMD and NVIDIA. These options come with varying levels of customization, so you can get the exact GPU model training setup that you need for your AI workloads and applications.
Why GPUs for model training?
AI model training—the process of feeding data to a specific algorithm to provide specific outcomes or support a specific use case—requires significant processing power. The development of graphics processing units provided a new hardware option that could handle the demands of AI model training and rapidly process high amounts of data with large numbers of cores and threads. Beyond on-premises deployments, cloud GPUs provide even more processing power as they are hosted in the cloud and can access a larger amount of virtualized, pooled computing power.
Benefits of GPU model training
For model training, GPUs provide a higher number of cores, more available memory, and increased processing speed compared to CPUs. The top benefits of GPUs for model training include:
Parallel processing capabilities
With multiple cores and threads, GPUs can parallelize computations and simultaneously complete data calculations, reducing overall task completion time. GPUs can also support high levels of data throughput and datasets with multiple dimensions.
Real-time processing
GPUs can decrease inference and support real-time processing capabilities, which is especially necessary for certain AI models that support autonomous vehicles, natural language processing applications, or healthcare diagnostic tools.
Scalability
GPUs are designed to scale easily and support distributed computing across a specific configuration or a data center. With GPU clusters, you can spread out your models across multiple processors and run distributed training for large, complex models such as GPT-4 or LLaMA and accompanying datasets.
Increased performance
Deep learning frameworks such as PyTorch, TensorFlow, and JAX are designed to run on GPU hardware. Using GPUs with these frameworks provides the optimal hardware to help them run effectively and efficiently.
Model scaling challenges and benchmarks
Even with the benefits GPUs provide for AI model training, several considerations remain, especially as models and datasets scale over time. They are:
- Data quality: Accurate AI models require clean, accurate, up-to-date data. As your model grows over time, implement data governance standards to avoid inaccurate data, duplicated data, or poorly processed data that could lead to inaccuracies.
- Resource allocation: Running processing- and memory-intensive models requires effective resource planning to avoid performance bottlenecks and maxed-out hardware. Having the right personnel on your team, MLOps frameworks in place, and knowing when to upgrade hardware helps ensure proper resource distribution.
- Model integration: AI model compatibility with your infrastructure and system tooling can change over time as the model grows, requiring updated hardware or specific software features. Using adaptable or modular AI models and software can make it easier to ensure interoperability and integration as you scale.
Benchmarks for GPU model training
When it comes to GPU model training, available industry benchmarks are still in development. Even so, several are already available to benchmark model performance. These are:
- ML PerfTraining: Designed to measure how fast systems can train models to a target quality metric.
- Lambda: Can focus on training throughput and inference speeds with PyTorch and TensorFlow.
- AIBench Training: Created to meet a wide range of AI use cases and address potentially conflicting requirements of early-stage industry benchmarks.
Training approaches for GPU model development
With GPU model training, you can either start with an already existing pre-trained model (such as BERT, GPT, or CLIP) or start from scratch and use one of the following options. Regardless of your starting point, you'll need to choose the right training methodology:
Supervised learning
This is the most structured option. You send the model labeled data sets, define key features, and set target variables to teach the model acceptable behavior. This type of training increases overall accuracy and reduces potential errors. Common use cases include speech and text recognition, spam filters, and fraud detection.
Unsupervised learning
Less structured, this model is fed data without any labels, parameters, or variables—and uses the algorithm to identify trends or decisions itself. This type of training is best suited for trend analysis, pattern identification, and process efficiency identification.
Reinforcement learning
This option is best suited for a specific goal or use case. This process involves having the AI model produce outputs and providing feedback (positive or negative) on output accuracy. It will also learn acceptable outputs over time. You can use reinforcement learning for use cases such as financial training, autonomous vehicles, automation, and natural language processing.
Deep learning
These neural networks are specific types of AI models that you can use for computer vision, language recognition, batch data analysis, large language models, and AI data processing. Specific models include convolutional neural networks, recurrent neural networks, auto-encoders, generative adversarial networks, diffusion models, and transformer models.
Benefits of using DigitalOcean GPU infrastructure
Easy to use
With DigitalOcean GPU Droplets and Bare Metal GPUs, you can easily access the computing power you need. GPU Droplets are available with just a few clicks in our New York, Toronto, and Atlanta data centers. You can easily Bare Metal GPU hardware in our New York and Amsterdam regions for full deployment customization.
Cost effective
With our transparent pricing model, you can access GPU computing power starting with on-demand GPU Droplets available at $0.76/hour and ready to support your AI/ML training and high-performance computing needs.
Reliable
You’ll have peace of mind knowing you’re backed by DigitalOcean’s enterprise-grade SLAs and 24/7 Support Team.
Serverless inference
If you choose to go with our Gradient™ AI Agentic Cloud offering, you’ll find it easy to quickly integrate available models from OpenAI, Anthropic, and Meta without the need to provision any hardware or additional setup.
Learn about DigitalOcean GPU availability
We’ve got you covered with our Gradient™ AI Agentic Cloud that provides customized, configurable, or out-of-the-box GPU setups and AI training tools so you can effectively train models to fit your desired use cases and easily integrate the features and tools you require.
GPU Droplets
Quickly access processing power to run AI models of all sizes with single to 8-GPU Droplet configurations.
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Available with NVIDIA H100, NVIDIA H200, NVIDIA RTX, and AMD Instinct GPUs.
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All GPU models offer a 10 Gbps public and 25 Gbps private network bandwidth.
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Designed specifically for inference, generative AI, large language model training, and high-performance computing.
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Regional availability in New York, Atlanta, and Toronto data centers
Bare Metal GPUs
Reserved, single-tenant infrastructure that gives you the ability to fully customize your AI hardware and software setup.
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Available with NVIDIA H100, H200, and AMD Instinct MI300X GPUs.
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Built for large-scale model training, real-time inference, and complex orchestration use cases.
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High-performance computing with up to 8 GPUs per server.
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Regional availability in New York (NYC) and Amsterdam (AMS) data centers
Gradient™ AI Platform
Our platform is designed to streamline GPU computing power and model selection, making it easy to move from testing to training and production.
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Quickly implement available models from OpenAI, Anthropic, Meta, and leading open-source providers.
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Serverless inference makes it easy to integrate AI models into your application without additional infrastructure setup.
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Access built-in evaluation tools to test prompts and workflows, score outputs, and monitor responses over time.
GPU model training FAQs
What is GPU model training?
GPU model training involves using graphics processing units to support the process of AI model training (feeding curated data to algorithms to provide accurate predictions and be tailored for specific industries or use cases). GPUs are often used for this task because they can handle the high-performance computing requirements, large data sets, and parallelized computational operations.
Which GPU is best for training models?
The best GPU for training models will depend on the model size, training requirements, supporting computing resources, and budget. Your main selection criteria should consider the number of available cores, available VRAM, and memory bandwidth. Both NVIDIA and AMD have a range of available GPUs designed to support AI model training.
How many GPUs do you really need for model training?
A general guideline for approximating the number of GPUs needed for model training is to take the model’s parameters in billions, multiply them by 18 (the factor of memory footprint) and 1.25 (the memory needed for activations), and divide them by the GPU size in GB.
It will look like this: Number of GPUs = (parameters in billions x 18 x 1.5)/GPU size in GB.