Whether you’re new to AI and machine learning (ML) or a seasoned expert, looking to train a large language model (LLM) or run cost-effective inference, DigitalOcean has a GPU Droplet for you. We currently offer seven different GPU Droplet types from industry-leading brands - AMD and Nvidia - with more GPU Droplet types to come. Read on to learn more about how to choose the right GPU Droplet for your workload.
Use cases: Large model training, fine-tuning, inference, and HPC
Why choose: AMD Instinct™ MI325X’s large memory capacity allows it to hold models with hundreds of billions of parameters entirely in memory, reducing the need for model splitting across multiple GPUs.
Key benefits:
Memory performance: High memory capacity to hold models with hundreds of billions of parameters, reducing the need for model splitting across multiple GPUs
Value: Offered at a competitive price point ($1.69/GPU/hr/contract) for a HPC GPU. Contact us to reserve capacity.
Key performance benchmark: With 256 GB of HBM3E memory (vs. MI300X’s 192 GB), MI325X can handle significantly larger models and datasets entirely on a single GPU
Use cases: Generative AI LLM training, fine-tuning, inference, and HPC
Why choose: AMD Instinct™ MI300X’s large memory capacity allows it to hold models with hundreds of billions of parameters entirely in memory, reducing the need for model splitting across multiple GPUs.
Key benefits:
Memory performance: High memory bandwidth (up to 5.3 TB/s) and capacity (192 GB of HBM3 memory) to efficiently handle larger models and datasets.
Value: Offered at a competitive price point ($1.99/GPU/hr on-demand) for a HPC GPU.
Key performance benchmark: Up to 1.3X the performance of AMD MI250X for AI use cases
AMD Instinct™ Resources:
Use cases: Training LLMs, inference, and high-performance computing
Why choose: NVIDIA H200 allows you to iterate and deploy models faster, offering faster inference speed than the H100s. It’s the first GPU with HBM3e memory, providing nearly double the memory capacity and bandwidth of the H100 for complex models.
Key benefits:
Iterate and deploy models faster: Up to 2x faster inference speeds than the NVIDIA H100 on LLMs like Llama 2 70B
Access larger memory capacity: Nearly double the memory capacity and bandwidth of the H100 for complex models
Key performance benchmark: Up to 2x faster inference and improved performance for memory-intensive HPC tasks vs. H100
NVIDIA H200 Resources:
Use cases: Training LLMs, inference, and HPC
Why choose: NVIDIA H100 is based on the NVIDIA Hopper architecture, specifically designed for next-generation AI and scientific computing tasks.
Key benefits:
Computing power: Improves AI computations by using mixed precision formats (FP8 and FP16).
Speed: Features 640 Tensor Cores and 128 Ray Tracing Cores, which facilitate high-speed data processing signature to the machine.
Key performance benchmark: Up to 4X faster training over NVIDIA A100 for GPT-3 (175B) models
NVIDIA H100 Resources:
NVIDIA RTX 4000 Ada Generation
Use cases: Inference, graphical processing, rendering, 3D modeling, video, content creation, and media & gaming
Why choose: NVIDIA RTX 4000 Ada is a versatile GPU with cost-efficient inference capabilities.
Key benefits:
Graphics performance: 3rd-generation Tensor Cores and next-gen CUDA cores with 20 GB of graphics memory and DLSS 3.0, which uses AI to boost frame rates while maintaining image quality.
Value: Offered at a competitive price point of less than $1 ($0.76 GPU/hr/on-demand).
Key performance benchmark: Up to 1.7X higher performance than NVIDIA RTX A4000
NVIDIA RTX 6000 Ada Generation
Use cases: Inference, graphical processing, rendering, virtual workstations, compute, and media & gaming
Why choose: NVIDIA RTX 6000 Ada Generation is a versatile GPU with cost-efficient inference capabilities.
Key benefits:
Graphics performance: 4th-generation Tensor Cores and next-gen CUDA cores with 48 GB of graphics memory and DLSS 3.0, which uses AI to boost frame rates while maintaining image quality.
Memory performance: 2X more memory than NVIDIA RTX 4000 Ada Generation.
Key performance benchmark: Up to 10X higher performance than NVIDIA RTX A6000
Use cases: Generative AI, inference & training, 3D graphics, rendering, virtual workstations, and streaming & video content
Why choose: NVIDIA L40S is a versatile GPU with cost-efficient capabilities for inference, graphics, digital twins, and real-time 4K streaming.
Key benefits:
Flexibility: 4th-generation Tensor Cores offer a highly-performant solution to use multiple NVIDIA libraries, such as TensorRT and CUDA.
Value: Offers 40% of the inference performance of the H100 at ~50% of the cost.
Key performance benchmarks: Up to 1.7X the performance of NVIDIA A100 for AI use cases
NVIDIA RTX 4000/6000 Ada Generation and L40S Resources:
No matter which GPU Droplet you require, when you choose GPU Droplets with DigitalOcean, you benefit from:
Scalable, on-demand GPU compute
Virtual instances to manage cost
Seamless integration with the broader DigitalOcean ecosystem, including access to our Kubernetes service
Pre-installed Python and Deep Learning software packages
Access to our optimized inference image, a pre-configured OS image with access a production-grade environment with built-in optimizations like CUDA and FlashAttention
HIPAA-eligibility and SOC 2 compliance (all GPU Droplets)
Flexible configurations from single-GPU to 8-GPU setup (select GPU Droplets)
Don’t hesitate - spin up a GPU Droplet today!
*Performance benchmarks available at amd.com and nvidia.com.
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