Training vs inference: two workloads with different hardware needs
Training teaches a model by adjusting parameters over many iterations, while inference applies the trained model to new data in a single forward pass. Because of this difference, training hardware is built for raw throughput, while inference hardware is tuned for low latency and cost per token.
Two phases of a model's life
AI training and AI inference are the two main workloads in machine learning. During training, a model learns patterns from data by iteratively adjusting its parameters through forward and backward passes. Inference then applies that learned model to new data with only a forward pass, as explained in IBM's AI inference article.
The purpose of inference is to generate outputs for real-world inputs. Red Hat's topic page describes inference as the final step of machine learning, where the model provides an answer based on data.
The two phases also differ in their performance goals. Training aims for accuracy and alignment, while inference targets speed, scalability, and cost-efficiency. According to Google Cloud's article, inference is the execution phase where each prediction is less computationally demanding than training, but serving many predictions requires highly optimized infrastructure.
Hardware for training emphasizes raw compute
AWS's Trainium page explains that Trainium is a purpose-built chip designed for AI training at scale, focusing on the economics of training large models. The hardware must handle massive datasets and complex neural networks.
Training workloads often span many chips in a data center. The Trainium page notes that the chip can scale from a single chip to millions, with the data center itself acting as an AI accelerator.
Effective training also requires high memory bandwidth and low-latency communication between chips. The Trainium page states that dedicated collective cores keep compute and communication separate, minimizing idle time.
Hardware for inference focuses on speed and cost
AWS's Inferentia product page states that the chip is designed for deep learning and generative AI inference, offering high throughput and the lowest cost per inference in Amazon EC2. Inferentia2 delivers higher throughput and lower latency compared to the first generation.
Inference optimization often uses techniques such as quantization, pruning, and layer fusion. NVIDIA's glossary also describes the token-based flow of large language model inference, including input processing and output generation, as seen on NVIDIA's AI inference page.
Inference can be deployed in the cloud or on edge devices. Edge deployment reduces latency and keeps data local, though it may require smaller models or specialized hardware.
Choosing hardware for each workload
The choice of hardware depends on the primary workload. If a model is frequently retrained, investing in training accelerators like Trainium can reduce iteration time. If the model is deployed for many predictions, inference-optimized chips like Inferentia may offer better cost performance.
Real-time applications such as chatbots or autonomous vehicles need low latency, which often favors edge inference or high-performance cloud servers. Batch jobs can tolerate higher latency and may use larger volumes of cheaper compute.
Some hardware is general-purpose and can run both training and inference, but specialized chips often deliver better efficiency. AWS provides separate families for each workload, reflecting the distinct hardware needs.
| Aspect | Training | Inference |
|---|---|---|
| Purpose | Build an accurate model | Make predictions on new data |
| Computation | Forward and backward passes over many iterations | Single forward pass |
| Hardware focus | High compute throughput, memory bandwidth | Low latency, cost per token |
| Optimization | Parameter updates, loss minimization | Quantization, pruning, batch processing |
| Deployment | Data centers with accelerators | Cloud, edge, or specialized chips |
Frequently asked questions
What is AI inference?
AI inference is the process of using a trained model to make predictions or generate outputs on new data. It performs a single forward pass and is optimized for speed and cost.
Why does training require more compute than inference?
Training involves iteratively updating model parameters over many forward and backward passes on large datasets, which demands more computational power and memory.
Can the same hardware be used for both training and inference?
Yes, general-purpose hardware like GPUs can run both, but specialized chips such as AWS Trainium for training and Inferentia for inference are optimized for each workload to improve efficiency.
What is edge inference?
Edge inference runs the model directly on devices like smartphones or sensors, reducing latency and keeping data on-device, which enhances privacy and reduces bandwidth use.