Deep Learning Inference Explained: From Theory to Real-World Applications

In the realm of machine learning, Artificial Intelligence (AI) inference is the deployment of a trained AI model to generate predictions using new data. Any instance of an AI model producing outputs or making decisions in a real-world AI application constitutes AI inference. Models undergo a "training" phase to excel on a dataset of sample tasks or data points. During this training, the model's parameters (and hyperparameters) are adjusted until its decision-making aligns with the patterns in the training data. While AI training aims for model accuracy and alignment, the primary goal of AI inference is to efficiently and cost-effectively deploy the trained model. There isn't a single "optimal" AI inference setup; instead, various methods exist for splitting workloads, utilizing different types of hardware (and computational algorithms), and accessing that hardware in diverse environments. The ideal setup depends on the specific use case and workload.

AI Inference vs. AI Training

Both AI inference and AI training involve a model making predictions about input data. However, training is where the "learning" occurs. In model training, a machine learning model generates predictions on a batch of training data examples. In supervised learning, a loss function calculates the average error (or "loss") of each prediction, and an optimization algorithm updates model parameters to reduce loss. This iterative process continues until loss is minimized to an acceptable level. AI training typically involves a forward pass, where the model generates an output for each input, and a backward pass, where potential improvements to the model's parameters are calculated. In AI inference, the trained model uses what it has “learned”-the model parameter updates that improved its performance on the training data-to infer the correct output for new input data. While training and inference are usually separate, they aren't mutually exclusive. For example, a social media platform's recommendation algorithm is trained on large datasets of user behavior and performs inference each time it suggests content to a user.

Types of AI Inference

There are many ways to execute AI inference, and therefore many ways to delineate its variants.

Online Inference

In online inference, a trained model processes input data immediately, one input at a time. This approach generally entails greater costs and complexity, especially for heavy workloads and the large neural networks that power deep learning models. However, it's often necessary for real-world use cases requiring real-time decision-making. For instance, a chatbot or self-driving car must process data in real time to avoid degrading the user experience.

Batch Inference

In batch inference, a trained model processes a large volume of inputs asynchronously in groups (or "batches"). Each batch is typically scheduled for a certain time. A business might use batch inference to run nightly reports on all of that day’s activity. This allows for greater flexibility and efficiency, making batch inference the more cost-effective option. It also allows for more efficient usage of hardware. GPUs, for example, contain many thousands of processing units (or "cores"), each of which can perform calculations simultaneously in parallel. Running inference for a single input that doesn’t enlist all those cores is a sub-optimal use of resources. Furthermore, model parameters must be loaded into system memory each time inference is performed, entailing energy usage and costs.

Micro-batching

There’s no clear, quantifiable batch size that differentiates “micro-batching” from “batching.” Instead, the two approaches are differentiated primarily by their goals: micro-batching aims to increase model throughput while (mostly) preserving model speed, whereas conventional batch inference aims to maximize efficiency and generally doesn’t take latency into consideration. Perhaps the most prominent application of micro-batching is in cloud-based LLM inference through major platforms such as Anthropic’s Claude or OpenAI’s ChatGPT.

AI Inference Deployment Locations

One of the most important considerations in designing an AI ecosystem is deciding where the inference workload will actually run.

On-Premise Deployment

On-prem deployment offers the greatest possible control over AI workloads, as you yourself have autonomy over how and when data is processed and computational resources are allocated. That control comes with a trade-off in the cost and labor involved. On-prem deployment, particularly with the hardware needed for enterprise-scale workloads and the massive models typically associated with generative AI, entails major upfront investment.

Cloud Deployment

In cloud deployment, models are run on remote servers managed by third-party vendors (such as IBM) in large data centers. This enables an organization to utilize high-powered AI hardware without the massive upfront investment required to purchase it or ongoing labor to maintain it. That flexibility and scalability comes with a trade-off in data sovereignty and, in some cases, latency and long-term costs. Data might travel back and forth to and from the cloud servers, which might have an adverse effect on inference speed (though that’s often negated by the more powerful hardware usually available through major cloud providers).

Edge Deployment

Broadly speaking, edge deployment can be understood as something akin to an “on-premise cloud.” It’s most beneficial when data needs to be aggregated from or distributed to a number of devices-such as sensors across a factory assembly line or monitoring devices in a hospital-and processed in near real-time. Those benefits are, to some extent, mitigated by the fact that edge computing usually enlists hardware that’s relatively limited compared to what’s available through cloud providers.

On-Device Deployment

On-device deployment is simple and secure, and theoretically provides the greatest possible user privacy. It is, of course, limited by the compute capacity of the device itself: the compute available in a smartphone, or even in a high-performance consumer computer, generally pales in comparison to that of specialized hardware.

Hardware for AI Inference

AI inference is a complex process that involves training an AI model on appropriate datasets until it can infer accurate responses. This is a highly compute-intensive process, requiring specialized hardware and software.

GPUs

GPUs were, as their name suggests, originally designed for rendering graphics (such as in video games). The ability to use that parallelism for math (instead of graphics) took a huge leap forward when NVIDIA introduced Compute Unified Device Architecture (CUDA), a software platform, API and programming model enabling developers to write code that runs directly on the thousands of parallel cores of GPU.

TPUs

TPUs are Google’s proprietary custom chips, built specifically for neural networks. Whereas GPUs are flexible, general purpose parallel processors, TPUs are designed exclusively for high-speed matrix math.

NPUs

Neural processing units (NPUs), like TPUs, were explicitly designed to process the computations of neural networks.

Read also: An Overview of Deep Learning Math

FPGAs

Field-programmable gate arrays (FPGAs) are a type of configurable integrated circuit that can be programmed (and reprogrammed) to suit the demands of specific applications, including artificial intelligence operations.

ASICs

ASICS, unlike FPGAs, cannot be customized or reconfigured. They’re explicitly designed to perform a single task at maximum efficiency.

Parallelism Techniques

The training or inference workloads of a large generative AI model will often exceed the capacity of even the largest accelerator hardware. When your workload is too big for a single GPU, it can be spread across multiple processors using one or more parallelism techniques to divide and spread out the work.

Data Parallelism

In data parallelism, a replica of the full model is copied across each processor. The input dataset itself is then split into multiple batches (or “shards”) and each copy of the model-that is, each processor-handles a single batch. While this is perhaps the most straightforward means of parallelism, it requires each processor to be large enough to fit all of the model’s parameters in memory. When dealing with larger LLMs and vision-language models (VLMs) with dozens or hundreds of billions of parameters, this is rarely possible.

Pipeline Parallelism

In pipeline parallelism, different layers of a neural network are assigned to different GPUs. For example, a 12-layer neural network might be divided across 3 GPUs, with the first GPU being assigned the first 4 layers, the second GPU handling the middle 4 layers and the third GPU handling the final 4 GPU layers. Efficient pipeline parallelism typically calls for mini-batching, so that each GPUs is always processing data simultaneously rather than sitting idly until it receives data from the previous GPU in the sequence. Naturally, a system using pipeline parallelism takes some “ramp-up” time to reach full device utilization.

Tensor Parallelism

For very large models, even a single layer might be too large to fit on a single processor. In tensor parallelism, the layers themselves are subdivided, with each processor receiving a portion of the tensor of model weights. Tensor parallelism significantly reduces the memory demands on each device, as each processor needs to load smaller tensors in memory than they would in other parallelism paradigms.

AI Training vs. Inference: A Recap

The AI training stage is when you feed data into your learning algorithm to produce a model, and the AI inference stage is when your algorithm uses that training to make inferences from data. Defining these two stages helps to show implications for AI adoption particularly with businesses.

Neural Networks: The Foundation of Deep Learning Inference

Neural networks, also known as AI models, get an education for the same reason most people do - to learn to do a job. More specifically, the trained neural network is put to work out in the digital world using what it has learned - to generate code or images, provide healthcare customer support, offer real-time translation, enable AI search across the web and more - in the streamlined form of an application. This speedier and more efficient version of a neural network infers things about new data it’s presented with based on its training. Inference can’t happen without training.

In the case of a code generation assistant, for example, a model might first learn to understand user intent and map relationships between natural language instructions and code. Deeper layers could recognize programming patterns, syntax and structures, while subsequent layers might be able to autocomplete functions, suggest snippets or even translate code into another language. At the final stages, the network could review code for accuracy, offering refinements and corrections. If the algorithm informs the neural network that it was wrong, it doesn’t get informed what the right answer is. Instead, the error is propagated back through the network’s layers, and the model must adjust its weights and try again. In a coding example, this might mean checking whether the suggested code actually runs, whether it follows the rules of the programming language and whether it does what the user asked for. With each pass, the network adjusts - weighing certain attributes of structure, logic or accuracy - and then tries again.

The Importance of Inference Optimization

Inference is not just about being fast - it’s about delivering the right combination of real-time responsiveness for each user while serving as many users as possible, all at AI factory scale. That’s why inference optimization techniques are so critical: they allow enterprises to optimize throughput and latency so they can meet their service level agreements across a variety of use cases.

Inference in Action: A Three-Phase Process

Prefill Phase: All input tokens are processed at once, flowing through every layer of the neural network. The model uses its trained weights to understand context, relationships and meaning. This is computationally heavy since the entire set of input tokens is processed all together.
Decode Phase (Token by Token): The model begins generating outputs one token at a time. Each new token depends on the history of all the previous ones.
Output Conversion (Tokens Out): The sequence of predicted tokens is decoded back into human-readable language, such as an image or audio.

The Growing Importance of Edge Inference

The next step for AI inference will be to break out of large cloud or data center environments and be possible on local computers and devices. This will enable more customization and control. Devices and robots will gain better object detection, face and behavior recognition, and predictive decision-making.

The Role of AI Inference in Various Industries

Industrial Systems: AI inference is becoming an important part of training industrial systems as well. For example, AI can be used for fast-paced visual inspection on a manufacturing line, freeing human inspectors to focus on flaws or anomalies identified by AI while lowering costs and improving quality control.
Robotics: Another common use of AI inference is robotic learning, popularized by the many attempts to perfect driverless cars.
Healthcare: AI inference is also assisting researchers and physicians.

AI Inference: The Operational Phase of AI

AI inference is the operational phase of AI, where the model is able to apply what it’s learned from training to real-world situations. AI’s ability to identify patterns and reach conclusions sets it apart from other technologies. Its ability to infer can help with practical day-to-day tasks or extremely complicated computer programming.

AI Inference Use Cases

Today, businesses can use AI inference in a variety of everyday use cases. These are a few examples:

Healthcare: AI inference can help healthcare professionals compare patient history to current data and trace patterns and anomalies faster than humans. This could be an outlier on a brain scan or an extra “thump” in a heart beat. This can help catch signs of threats to patient health much earlier, and much faster.
Finance: After being trained on large data sets of banking and credit information, AI inference can identify errors or unusual data in real-time to catch fraud early and quickly. This can optimize customer service resources, protect customer privacy, and improve brand reputation.
Automotive: As AI enters the world of cars, autonomous vehicles are changing the way we drive. AI inference can help vehicles navigate the most efficient route from point A to point B or brake when they approach a stop sign, all to improve the ease and the safety of those in the car.

Many other industries are applying AI inference in creative ways, too. It can be applied to a fast food drive-through, a veterinary clinic, or a hotel concierge.

Types of AI Inference in Practice

Different kinds of AI inference can support different use cases.

Batch inference: Batch inference gets its name from how it receives and processes data: in large groups. Instead of processing inference in real time, this method processes data in waves, sometimes hourly or even daily, depending on the amount of data and the efficiency of the AI model. These inferences can also be called “offline inferences” or “static inferences.”
Online inference: Online inference or “dynamic” inference can deliver a response in real time. These inferences require hardware and software that can reduce latency barriers and support high-speed predictions. Online inference is helpful at the edge, meaning AI is doing its work where the data is located. This could be on a phone, in a car, or at a remote office with limited connectivity. OpenAI’s ChatGPT is a good example of an online inference-it requires a lot of upfront operational support in order to deliver a quick and accurate response.
Streaming inference: Streaming inference describes an AI system that is not necessarily used to communicate with humans. Instead of prompts and requests, the model receives a constant flow of data in order to make predictions and update its internal database.

The Challenges of AI Inference

The biggest challenges when running AI inference are scaling, resources, and cost.

Complexity: It is easier to teach a model to execute simple tasks like generating a picture or informing a customer of a return policy. As we lean on models to learn more complex data-like how to catch financial fraud or identify medical anomalies-they require more data during training and more resources to support that data.
Resources: More complex models will require specialized hardware and software to support the vast amount of data processing which takes place when a model is generating inferences. A key component of these resources is central processing unit (CPU) memory. A CPU is often referred to as the hub or control center of a computer. When a model is preparing to use what it knows (training data) to generate an answer, it must refer back to the data which is held in CPU memory space.
Cost: All of these puzzle pieces that make AI inference possible are not cheap. Whether your goal is to scale or to transition to the latest AI-supported hardware, the resources it takes to get the full picture can be extensive. As model complexity increases and hardware continues to evolve, costs can increase sharply and make it tough for organizations to keep up with AI innovation.

vLLM: A Solution for Faster LLM Inference

A specific inference engine known as vLLM, helps keep these challenges at bay. vLLM speeds up the output of generative AI applications by making better use of the GPU memory. vLLM is a library of open source code maintained by the vLLM community. It helps large language models (LLMs) perform calculations more efficiently and at scale. It uses tools like LLM Compressor to help you inference faster, taking a big burden off of your team and resources.

As an open source solution, vLLM allows companies to:

Own and manage their GPUs.
Control their data.
Experiment with state-of-the-art models as soon as they are released.

vLLM can be deployed across a variety of hardware including NVIDIA and AMD GPUs, Google TPUs, Intel Gaudi, and AWS Neuron. vLLM is also not restricted to specific hardware, meaning it works across the cloud, in the data center, or at the edge.

Distributed Inference: Dividing the Workload

Distributed inference lets AI models process workloads more efficiently by dividing the labor of inference across a group of interconnected devices. Think of it as the software equivalent of the saying, “many hands make light work.” Distributed inference supports a system that splits requests across a fleet of hardware, which can include physical and cloud servers. From there, each inference server processes its assigned portion in parallel to create an output.

The Machine Learning Inference Pipeline: From Data to Decisions

Machine learning inference is the phase where a trained model is used to make predictions on new, unseen data. Instead of learning, the model applies what it has already learned to generate outputs that inform decisions.

The process looks straightforward:

Input: New data flows in, such as an image, a sentence, or a transaction log.
Model processing: The ML model applies its trained weights and parameters.
Output: A prediction or decision is produced. Examples include “fraud likely,” “positive sentiment,” or “maintenance needed.”

Running inference at enterprise scale requires more than one step. Models need a pipeline that ensures predictions are accurate, secure, fast, and cost-effective. Think of it as a factory assembly line. Each stage has a role, and weak spots create bottlenecks.

Here are the nine stages of a reliable ml inference pipeline:

Data Collection: New data arrives from APIs, sensors, logs, or user interactions. The challenge is capturing data at high velocity and in multiple formats.
Data Preprocessing: Data is cleaned, normalized, and formatted to meet the model’s expectations.
Feature Engineering: Raw data is transformed into features that improve prediction quality.
Model Loading: The inference engine retrieves the correct model from a registry.
Input Validation: Requests are checked for schema, format, and value ranges. Invalid inputs are rejected or transformed.
Prediction Execution: This is the core step. The model generates predictions, optimized for latency and cost.
Postprocessing: Raw outputs are converted into usable results.
Monitoring and Logging: Enterprises must track inference in real time.
Scaling and Optimization: Inference workloads surge. The system must adapt automatically.

Key Steps for Deploying Machine Learning Models for Inference

Deployment is where theory meets reality. These are the core steps enterprises must master when deploying machine learning models for inference:

Model packaging: Convert trained models into formats like ONNX. Containerize with Docker for consistency.
Infrastructure setup: Use Kubernetes to orchestrate workloads across cloud and on-prem environments.
API integration: Expose inference endpoints with REST or gRPC.
Security and compliance: Add authentication, encryption, and audit logging.
Performance optimization: Use pruning, quantization, and caching to reduce latency and cost.
Continuous monitoring: Track latency, throughput, and drift. Retrain when performance drops.
Multi-environment scaling: Deploy to cloud, hybrid, and edge environments depending on latency and compliance needs.

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