An AI accelerator is a specialized processor designed to perform the types of mathematical operations that AI and machine learning workloads require far more efficiently than a general-purpose CPU. Rather than handling a wide variety of computing tasks, AI accelerators are purpose-built to maximize throughput on the parallel calculations that underpin neural network training and inference.
The most widely deployed AI accelerators are graphics processing units (GPUs), which were originally developed for rendering graphics but proved equally well-suited to the parallel processing demands of AI. Other accelerator types include tensor processing units (TPUs), field-programmable gate arrays (FPGAs), and application-specific integrated circuits (ASICs), each offering different tradeoffs between performance, flexibility, power consumption, and cost.
Common Types of AI Accelerators
- GPU (Graphics Processing Unit). The dominant accelerator for AI training and inference. Thousands of parallel cores make GPUs highly effective for large-scale matrix operations. NVIDIA's H100 and B200 are widely used examples in data center deployments.
- TPU (Tensor Processing Unit). A custom ASIC developed by Google and optimized specifically for TensorFlow-based machine learning workloads. TPUs offer high throughput for inference and training at scale within Google's infrastructure.
- FPGA (Field-Programmable Gate Array). A reconfigurable chip that can be programmed after manufacturing to accelerate specific workloads. FPGAs offer lower latency than GPUs for certain inference tasks and are commonly used in edge and real-time applications.
- ASIC (Application-Specific Integrated Circuit). A chip designed from the ground up for a single purpose. ASICs can deliver the highest efficiency for a specific workload but lack the flexibility of GPUs or FPGAs. Examples include Google's TPUs and AWS's Inferentia chips.
AI Accelerators and Data Center Infrastructure
AI accelerators — particularly high-end GPUs — place infrastructure demands on data centers that are fundamentally different from traditional CPU-based servers. A single accelerator-dense server can draw 10 kW or more, and fully populated high-density GPU racks can exceed 100 kW, pushing well beyond what conventional air-cooled infrastructure supports. This drives adoption of direct-to-chip cooling, liquid immersion cooling, and other advanced data center liquid cooling approaches.
Managing accelerator infrastructure requires precise visibility into power consumption, thermal conditions, physical connectivity, and capacity. Data Center Infrastructure Management (DCIM) software gives data center managers the tools to track accelerator assets and their relationships, monitor real-time power and environmental conditions, model capacity before deploying new hardware, and identify hot spots before they cause downtime.
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