Accelerator Card

An accelerator card is specialized hardware that offloads and speeds up a specific class of computation that a general-purpose CPU handles slowly. Accelerators are common in high-performance computing (HPC), where they dramatically accelerate scientific simulations, machine learning, data analytics, cryptography, and high-frequency trading. They use parallel processing and workload-specific designs to outperform CPUs, and usually work alongside a CPU rather than replacing it.

What is an accelerator card?

A CPU is built to handle a wide range of tasks, which makes it flexible but not optimal for workloads that demand intensive, repetitive computation. An accelerator card is designed specifically for such workloads, trading generality for speed. Accelerators are typically stand-alone devices that plug into a system (often via PCIe) and perform their specialized task, then hand results back to the CPU.

Accelerators span a spectrum from general to specialized. GPUs and FPGAs are general-purpose accelerators: a GPU speeds up a broad variety of HPC workloads such as computational fluid dynamics, molecular dynamics, and AI training, while an FPGA can implement cryptography, digital signal processing, or even whole custom systems-on-a-chip. At the specialized end, an ASIC (application-specific integrated circuit) implements a single algorithm in silicon for maximum speed.

Types of accelerator cards

• GPUs, massively parallel processors, the most widely used HPC accelerator and the standard for AI.
• FPGAs, reprogrammable logic chips tailored to a specific algorithm.
• Machine learning accelerators, devices built purely for AI, such as Google's Coral, Intel/Habana's Gaudi, the Cerebras WSE, and Graphcore's IPU; these iterate on GPU design paradigms but hone in on AI as the sole focus.
• Cryptographic and SSL accelerators, programmable cards that offload encryption and security operations to improve server performance.
• Graphics and vector accelerators, cards focused on video, 3D rendering, or vector math for AI and data applications.

Why accelerator cards matter

Accelerators let systems reach performance and efficiency levels a CPU cannot. Because they use purpose-built algorithms and hardware, they perform complex operations that would take a general processor far longer, and they do so at lower energy cost per calculation. This makes them a foundational tool for improving computing performance and enabling new research and applications.

Accelerators vs. general-purpose processors

	Accelerator card	CPU
Design goal	One workload class, done fast	Any workload, done adequately
Parallelism	High	Limited
Efficiency on target work	Very high	Lower
Role	Offload engine	General coordinator

Accelerators and CPUs are used together: the CPU manages the overall program while the accelerator handles the heavy, repetitive computation.