Build AI applications that respond in milliseconds with low latency inference on DigitalOcean infrastructure
Your AI is only as good as how fast it responds. Whether you're building a trading algorithm that needs to react in microseconds or a customer service bot that can't leave users hanging, latency kills the experience. Run your AI agents, workloads, and models with our GPU Droplets and Gradient platform with agent creation and serverless inference capabilities.
Low latency inference: Essential for high-speed AI performance
Production-grade AI models must have the ability to regularly integrate new data and quickly make decisions; this requires low latency inference for your team and your users to get prompt responses. Setting up your infrastructure to support low latency inference provides the necessary performance and support for all your customer-facing AI applications, from customer chatbots to real-time fraud detection. DigitalOcean’s DigitalOcean’s Gradient™ AI Agentic Cloud provides access to simple, secure, and scalable GPU computing power and serverless inference capabilities so you can configure the right solution for your AI applications.
What to know about low latency, inference, and implementation
AI model functionality relies on how quickly it can ingest new data, update its model, and make decisions with its ever-evolving data. Tracking inference latency will help you avoid performance and data processing issues.
What are the benefits of low latency inference?
Low latency inference means your AI models and applications can quickly take in new information and make decisions without unnecessary lag time. For customer-facing use cases, you’ll notice:
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Better user experience: When latency is over 120 ms, users can start to notice application slowness, which can lead to frustration or task abandonment. Focusing on low latency inference means users can get the answers or complete tasks within their desired timeframes, resulting in higher customer satisfaction.
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Higher engagement: Lower latency can better facilitate smoother, satisfying customer interactions that increase the likelihood of brand loyalty and overall interaction with company chatbots, personalization engines, as well as mobile and web applications.
Internally, low latency inference means:
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Efficient resource usage: Lower latency models reduce the amount of required computing power per inference, necessitating less overall computing power and using less energy.
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Higher throughput: Shorter inference times maximize processing throughput as GPUs can run more requests per second.
Real-time application support: Low latency inference requires less computing power and energy than complex, high latency models, so you can run them on edge devices or computing systems that continuously process new data with limited hardware resources.
Low latency inference powers real-time AI use cases
Low latency inference is essential for real-time data integration for dynamic AI models in industries such as health care, finance, automotive, and customer service. Specific use cases include:
Autonomous vehicles
Requires ultra-low latency inference and computer vision to detect and classify objects (street signs, pedestrians, obstacles) while in motion. Without the ability to quickly ingest new data, these vehicles pose safety concerns on the road.
Customer service chatbots
Needs low latency inference to keep customers engaged and provide relevant answers in a timely manner. Slow or inaccurate responses increase customer frustration and can harm brand loyalty.
Patient experience in healthcare
Relies on low latency inference for real-time patient monitoring, diagnostic decisions, and patient chatbot support. Response delays can potentially impact patient care or result in slower diagnoses and increased health complications.
Financial trading
Implements low latency inference to complete high-frequency trading, risk monitoring, along with fraud detection and prevention. High latency models can result in overlooked suspicious activity or missed financial opportunities.
Factors that affect latency
The right GPU configuration and setup will support your AI workflows on the front and back end, so you can adjust model size, process data, and fine-tune model and infrastructure setups so that latency is minimal for both you and your users. Even so, there are a number of factors to consider in order to achieve low latency and maximize performance. Inference latency changes based on:
Model complexity
The larger and more connected your LLM or neural network is, the more computations it requires to complete tasks, which increases overall latency. The architecture choice can also affect model complexity as it includes code for the feature extraction network (backbone) and prediction features (detection head).
Hardware processing speed
How quickly your hardware can ingest, process, and integrate new data into your AI applications will determine how rapidly your model can respond to user input.
Software optimization and frameworks
An optimized inference engine can help increase overall performance. Examples include NVIDIA Tensor RT and Intel’s OpenVINO. You can also access optimization tools in PyTorch and TensorFlow frameworks.
Batch size
Ingesting multiple inputs simultaneously can mean larger outputs and increased throughput, but can reduce latency for individual data sets. Most real-time applications have batch sizes of one for minimal latency.
Model size
Keeping model data relevant and accurate is a straightforward way to keep latency at a minimum. You can reduce the numerical precision (quantization) and remove redundant parameters (pruning) to keep model size and computational requirements low.
Latency benchmarking for AI models and architecture considerations
Different model types require different benchmarking tests for inference effectiveness, and there are multiple metrics you can use to measure latency and inference from NVIDIA, LLMPerf, and MLCommons. Common metrics are:
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Time to first token: How long a user waits to get an output from the model.
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End-to-end request latency: Time period between query submission to receiving a full response. This metric accounts for network latencies and queuing/batching mechanisms.
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Inter-token latency: Average time between consecutive token generations. Some benchmarks will include time to first token in this calculation, while others do not.
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Tokens per second: Measures total output of tokens per second and accounts for simultaneous requests.
Beyond specific metrics, model architecture components can affect benchmarking performance. Here’s how that might look:
| Architecture Factor | Impact on Benchmarking Metrics |
|---|---|
| Model size (parameters) | Larger models (e.g., GPT-3) have higher latency and lower throughput unless heavily optimized. |
| Layer types (e.g., CNN vs Transformer) | Certain architectures are more parallelizable (e.g., CNNs on GPUs), affecting throughput and latency. |
| Depth vs. width (number of layers vs. neurons in a layer) | Deeper models can increase latency; wider models may benefit from parallel compute. |
| Attention mechanisms | Transformers scale quadratically with input length (O(n²)), affecting latency and memory. |
| Tokenization strategy | Affects input length, which directly influences inference time and memory usage. |
| Activation functions | Simpler functions (ReLU) benchmark faster than complex ones (Swish, GELU). |
| Quantization compatibility | Architectures that support INT8/FP16 quantization run faster on hardware accelerators. |
| Batching behavior | Some models benefit more from batching during inference, which improves throughput benchmarks. |
Achieve low latency inference with DigitalOcean’s Gradient portfolio
Our Gradient offerings make it easy for you and your team to spin up GPU-based infrastructure that has the computing power to achieve low latency inference and keep your AI models and workloads running as intended.
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 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 compute with up to 8 GPUs per server.
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Regional availability in New York (NYC) and Amsterdam (AMS) data centers.
Gradient Platform
Our platform is designed to help run AI workloads all the way from development to production.
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Quickly implement available models from OpenAI, Anthropic, Meta, and leading open-source providers.
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Run serverless inference with unified model access, data protection, and automatic infrastructure scaling.
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Helps you build and monitor intelligent agents with serverless model integration, function calling, RAG, external data, and built-in evaluation tools.
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Offers functionality to test and improve agent behavior and regularly integrate new data.
Low latency inference FAQs
What is inference latency?
Latency in inference is the amount of time between an AI model receiving new data and producing a result or prediction.
What does low latency indicate?
Low latency indicates that your infrastructure can support high volumes of data with minimal delay. For inference, it means your AI model is optimized and can easily handle new data and quickly compute predictions or make decisions in real time.
How fast is "low latency" exactly?
Low latency is anything under 100ms and ultra-low latency is anything under 30 ms. However, optimal low latency speeds can vary across specific use cases and industries.
What are the benefits of low latency?
Low latency brings benefits for customer-facing applications such as improved user experience, faster response times for queries, and overall increased user satisfaction and engagement. For your infrastructure, having low latency inference means more efficient resource use, and the ability to support real-time use cases.
Why is low latency important in AI?
Latency directly affects application and system performance. Having low latency means that your model responds in a timely manner and can make real-time decisions, especially for AI use cases that use large datasets and require the model to quickly generate responses and decisions for users.
How can I reduce inference latency?
You can reduce inference latency through the use of hardware designed to support parallel processing, large data sets, implement frameworks such as PyTorch and TensorFlow, and practice good data hygiene to prune down AI models as they grow in complexity.