What is Inference vs. Training?

What is Inference vs. Training?

Artificial intelligence workloads fall into two main categories: training and inference. Each has different performance demands and requires a tailored system architecture. IronGPU builds specialized servers for both phases to deliver unmatched results.

Training: Build the Intelligence

Training is the process of teaching an AI model by processing large datasets through complex computations. This phase is highly demanding on both GPUs and system bandwidth, making it ideal for high-density, multi-GPU IronGPU training servers.

• Purpose: Build and refine AI models using massive datasets
• Recommended Hardware: IronGPU Training-Class Servers
• Key Specs: 4 to 10 GPUs, high core-count CPUs, fast NVMe storage, PCIe Gen4/Gen5 support, advanced cooling

Inference: Deploy the Intelligence

Inference is the application phase — using a trained model to make predictions or decisions in real time. IronGPU inference systems are engineered for low latency, energy efficiency, and scalable performance across edge or cloud environments.

• Purpose: Execute trained models to deliver real-time results
• Recommended Hardware: IronGPU Inference-Class or Edge Servers
• Key Specs: 1 to 4 GPUs, compact chassis, low-power CPUs, optimized I/O for latency-sensitive use cases

IronGPU: Built for the Full AI Lifecycle

From model development to global deployment, IronGPU provides systems built specifically for the phase you're in. Whether you're training massive transformer models or running edge inference for surveillance, IronGPU delivers purpose-built, performance-tuned servers you can rely on.

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