High throughput, low latency GPU inference with DigitalOcean
Need high-performance computing power for your inference tasks and access to scalable, on-demand GPU infrastructure? Look no further than our GPU Droplets and Bare Metal GPUs.
Accelerate AI/ML workloads with GPU inference
What is GPU inference?
Inference—the process of using a trained model to process and integrate new data—is an essential part of running AI systems in live environments… However, it also requires the right hardware to effectively process data and generate responses in near real-time at low latency.
GPU inference involves using GPUs to accelerate inference tasks and reliably provide parallel computing and high data throughput. This makes it ideal for AI/ML use cases such as chatbots, real-time video or image analysis, self-driving cars, recommendation systems, or even fraud detection.
GPU vs CPU for inference
It’s possible to use both CPUs and GPUs for inference. Your hardware selection will depend on your computing requirements, available data, and AI model size. Overall, CPUs are ideal for inference tasks with smaller AI models or those that require higher precision levels. GPU inference is best for real-time data processing, large-scale data handling, or use cases with high throughput.
| Factor | CPU | GPU |
|---|---|---|
| Architecture | Single-threaded processing units are designed for general-purpose processing and sequential processing tasks. | Multi-threaded processing units are created for parallel computing tasks, where complex processes can be broken down and simultaneously run as smaller tasks. |
| Accuracy and precision | Mainly use 32- or 64-bit floating point precision. Can provide high accuracy, but results in slower performance. | Often use 16-bit floating point precision, which can be faster but with slightly less accuracy. |
| Energy efficiency | Can be efficient for smaller tasks and models. | Can consume more power due to the scale of parallel processing workloads. |
| Performance | Sequential processing means CPU inference is best for small batch, high precision tasks. | Parallel processing means GPU inference can reduce processing time for large data sets and real-time use cases. |
| Use cases | Best for high-precision, small model deployments, including edge devices and mobile applications. | Suited for high throughput and large-scale data handling, such as autonomous vehicles or real-time analytics. |
GPU inference deployment strategies
Effective inference deployment relies on proper infrastructure to support the tasks and realistic planning. Figuring out how and when to run large language models means considering scale, security, reliability, latency, cost, and any project-specific requirements.
Inference strategy
To determine the optimal deployment strategy, weigh the cost of inference infrastructure against the time it takes to complete tasks and the value delivered to customers. Available frameworks to run these different types of inference include TensorFlow Serving, ONNX Runtime, Kubeflow, PyTorch, and OpenVINO.
Real-time inference
This type creates predictions or classifications based on low-latency live data to produce immediate responses to an event or user input. The model ingests the data through a REST endpoint and then provides its predictions for these time-sensitive applications.
Batch inference
This option serves predictions for large data volumes in bulk at scheduled intervals or offline. The input data is stored in a data lake or warehouse, processed all together, and then sent to the online model for predictions or inferences.
Streaming inference
This setup continuously processes data input in real time from a data source. It then processes the data, runs it on the AI model, and creates its predictions. This is best for low-latency, high-throughput use cases.
Optimization techniques for GPU inference
Various factors affect GPU inference performance and lengthen model processing time in production, including hardware and OS configurations, the number of simultaneous data inputs, application dependencies, and unexpected data. Because inference adds data to an AI model in real time, it should run as close as possible to its initial baseline. Some techniques to optimize inference tasks include:
-
Caching: Stores intermediate computations or inference results for faster data retrieval.
-
Early exit mechanisms: Computes all possible predictions before processing the model layers. Once the model finds a high-confidence prediction, it stops calculating possibilities and produces an output.
-
Knowledge distillation: Transfers knowledge from a large teacher model to a smaller student model.
-
Low-rank factorization: Breaks down large matrices into smaller ones.
-
Memorization: Stores function calls so they can easily be reused.
-
Pruning: Eliminates unnecessary model parts to reduce overall size and complexity.
-
Quantization: Reduces the precision of model weights and activations to smaller data types.
-
Weight sharing: Shares weights across multiple neurons or layers.
Beyond these techniques, you can also run multiple parallel workloads and batch model processing.
Benefits of using DigitalOcean for GPU inference
Scalable
With on-demand infrastructure available in a few clicks, you can access GPU Droplet configurations from a single GPU up to 8 GPUs. Our Bare Metal GPUs are available for reservation and can be provisioned in approximately 1-2 days instead of weeks.
Reliable
All GPU Droplets are HIPAA-eligible and SOC 2 compliant and supported by enterprise-grade SLAs to keep all of your workloads running and online.
Cost effective
You’ll benefit from our transparent pricing policies that allow you to pay for GPU computing power per hour and per GPU. Bare Metal GPUs are available at competitive prices for custom deployments.
Open source
Built with open source standards, our GPU Droplets are compatible with projects that support open source OSes, log management, storage, and containers. They also come pre-installed with Python and Deep Learning software packages.
Learn more about DigitalOcean Gradient
Harness DigitalOcean GPU computing power through our GPU Droplets, Bare Metal GPUs, and Gradient platform.
GPU Droplets
Deploy AI-ready infrastructure with just a few clicks and access pre-configured GPU infrastructure that is ready to scale as needed.
-
Available with NVIDIA H100, NVIDIA RTX, and AMD Instinct GPUs.
-
All GPU models offer a 10 Gbps public and 25 Gbps private network bandwidth.
-
Regional availability in New York, Atlanta, and Toronto data centers.
Bare Metal GPUs
Fully customizable infrastructure deployments that you can design for all of your AI workload requirements.
-
Offer single-tenant, dedicated infrastructure with no data center neighbors.
-
Suited for large-scale model training, real-time inference, and complex orchestration.
Regional availability in New York (NYC) and Amsterdam (AMS) data centers.
Gradient Platform
Build, scale, and deploy AI workloads through DigitalOcean’s unified cloud AI platform.
-
Can support HPC, inference, agent creation, and AI workflows.
-
Helps you build and monitor intelligent agents with serverless model integration, function calling, RAG, external data, and built-in evaluation tools.
-
Has access to preconfigured agents that support specific use cases, such as automation and application support.
GPU inference FAQs
What is GPU inference?
GPU inference is the process of using GPUs to complete inference tasks. Inference is where you run live data through a trained AI model to have it make a prediction or solve a specific task.
GPUs are used because they can handle the real-time processing requirements for inference.
Which GPU is best for AI inference?
The best GPU for inference depends on support for frameworks like TensorFlow and PyTorch, memory capacity, interconnectivity, and thermal design power, which all affect performance. Industry options include offerings from NVIDIA and AMD. You can access these offerings with DigitalOcean GPU Droplets.
How does GPU inference differ from GPU training?
GPU inference is adding new data to an AI model while it is in production to complete a specific task. GPU training is completed in the early stages of AI model development. An example would be a self-driving car identifying a stop sign on a new road outside the lab (inference) versus learning how to identify a stop sign and what it is during model development (training).
How can you speed up GPU inference?
You can speed up GPU inference through AI model compression and optimization. Specific tasks to take include model pruning, knowledge distillation, low-rank optimization, and using smaller-sized AI models.