Build your AI/ML products on the right infrastructure with AI training GPUs
Train AI models that power breakthrough products with dedicated AI training GPUs.
Stop letting hardware limitations slow your AI innovation
Limited GPU access turns quick experiments into multi-week bottlenecks. Traditional infrastructure cannot handle modern AI workloads’ massive parallel processing demands of use cases like computer vision, large language model training, and real-time inference.
AI training GPUs include specialized processors with CUDA cores and streaming multiprocessors (SMs) that excel at parallel computation, which is essential for neural network training. With Gradient GPU Droplets, your tech team can support workloads for use cases such as natural language processing and reinforcement learning across industries like healthcare and finance.
Enterprise AI infrastructure made simple
Deploy scalable computing resources and high-performance training environments that accelerate model development from prototype to production. Leverage distributed GPU clusters, automated data pipelines, and comprehensive validation frameworks to ensure your AI models meet enterprise-grade performance and reliability standards.
Memory specifications
Deep learning models require substantial VRAM across the GPU memory hierarchy. Understanding the speed-capacity tradeoff is crucial—registers provide the fastest access for frequently used variables, while global memory offers a larger capacity for model parameters.
Architecture selection
With Gradient GPU Droplets, choose from NVIDIA H100 for accelerated LLM training with up to 4X faster performance, the newly launched AMD Instinct™ MI325X with 256GB HBM3E memory and 1.3x greater compute performance for holding massive models in memory, AMD Instinct™ MI300X with high memory bandwidth for large model training and 1.3X better AI performance than MI250X, or NVIDIA RTX series (4000 Ada, 6000 Ada) and L40S for versatile inference, creative workflows, and cost-efficient AI applications. Gradient GPU Droplets provide up to 75% cost savings vs. hyperscalers with 2-click deployment in under a minute, starting at $1.69/GPU/hr for MI325X configurations.
Development environment
Access pre-configured software stacks with PyTorch, TensorFlow, JAX, and Hugging Face libraries. GPU virtualization platform supports containerized deployments for reproducible training environments across different projects.
Single vs multi-GPU setups
Individual GPU configuration
Ideal for model prototyping, small to medium-sized models, and cost-conscious projects. The best budget GPU for AI options provides excellent performance for individual researchers with a simplified setup and debugging.
Distributed training systems
This setup is essential for large-scale model training with reduced processing times. Multi-GPU training infrastructure supports data and model parallelism, enabling training beyond single-GPU memory limits through gradient synchronization and load balancing.
Use cases and model types
Transform your business operations with specialized AI models tailored for diverse applications, including natural language processing, computer vision, predictive analytics, and automated decision-making systems.
Large language model development
Train transformer-based models and perform fine-tuning using our LLM training GPUs. Distributed training capabilities enable the development of models with billions of parameters across multiple nodes.
Computer vision applications
Develop image classification, object detection, and generative models using specialized deep-learning GPU infrastructure. Handle convolutional neural networks, vision transformers, and diffusion models with optimized performance.
Research and experimentation
Conduct AI research with flexible resource allocation for hyperparameter tuning and model architecture exploration. Cloud GPUs for AI workloads support rapid prototyping and validation.
Gradient GPU Droplets features and benefits
Advanced hardware architecture
Access the latest GPU technologies with Tensor Cores for accelerated matrix operations, sophisticated memory hierarchies including L1, L2, and constant cache levels, plus optimized register files for maximum data reuse and performance efficiency. DigitalOcean's Gradient GPU Droplets provide enterprise-grade hardware solutions featuring:
NVIDIA H100 GPUs with up to 4X faster training over NVIDIA A100 for GPT-3 (175B) models
AMD Instinct™ MI300X delivering up to 1.3X the performance of AMD MI250X for AI use cases
NVIDIA RTX 4000 Ada Generation offering up to 1.7X higher performance than NVIDIA RTX A4000
High memory bandwidth and capacity (up to 1536 GB GPU memory) to efficiently handle larger models and datasets
Deployment flexibility
You can choose from on-demand access, reserved instances, and spot pricing models. The GPU virtualization platform supports both bare metal and containerized deployments. DigitalOcean's platform offers unparalleled flexibility with:
Zero to GPU in just 2 clicks: Get a GPU Droplet running in under a minute
Cost-effective pricing: Up to 75% cheaper than AWS for on-demand H100s and H200s with 8 GPUs each
Multiple GPU configurations: From single-GPU instances to 8-GPU clusters for enterprise workloads
Global availability: Deploy across NYC2 TOR1 and ATL1 data centers with more locations coming soon
High-performance networking: 10 Gbps public and 25 Gbps private network bandwidth
Infrastructure management
Focus on AI development while we handle monitoring, scaling, and optimization. AI infrastructure hosting includes automatic resource allocation and performance tuning without manual intervention.
Simplified management: The same easy-to-use platform that has delivered cloud needs for over 10 years
Enterprise reliability: HIPAA-eligible and SOC 2 compliant products backed by enterprise-grade SLAs
24/7 support: Trusted support team to keep you online and productive
Comprehensive storage solutions: Up to 40 TiB NVMe scratch disk for high-performance workloads
Serverless inference options: Access serverless inference API and agent development toolkit
FAQ
What is an AI training GPU?
An AI-training GPU is a specialized processor optimized for machine learning workloads. It features parallel processing through CUDA cores and streaming multiprocessors. These GPUs include Tensor Cores for matrix operations, sophisticated memory hierarchies with registers and cache levels, and high memory bandwidth for handling large datasets.
Which GPU is best for training LLMs?
NVIDIA H100 GPUs excel at large language model training with Thread Block Clusters, Tensor Memory Accelerator (TMA) for efficient data transfer, and up to 80GB memory capacity. A100 GPUs perform strongly for most LLM training tasks with robust parallel processing capabilities.
Can I train models with a consumer GPU?
Consumer GPUs handle small AI models but lack memory capacity and specialized features for production-scale training. Professional AI training GPUs provide substantially more VRAM, better memory bandwidth, and enterprise reliability compared to consumer alternatives.
How do I know if my GPU is suitable for training AI models?
Key indicators include sufficient VRAM, high memory bandwidth, AI-specific features like Tensor Cores, and optimized memory hierarchy with fast register and cache access. The GPU should support parallel processing through multiple streaming multiprocessors and CUDA cores.
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