GPUs, TPUs, and FPGAs: Hardware Accelerators Explained

Hardware accelerators are chips built to speed up specific tasks. They work with a traditional CPU to handle heavy workloads more efficiently. In data centers, on the cloud, and at the edge, GPUs, TPUs, and FPGAs are common choices for accelerating machine learning, graphics, and data processing.

GPUs have many small cores that run in parallel. This design makes them very good at matrix math, image and video tasks, and training large neural networks. They come with mature software ecosystems, including libraries and tools that help developers optimize performance. The trade‑off is higher power use and a longer setup time for very specialized workloads.

TPUs, created by Google, are optimized for tensor operations used in modern ML. They deliver fast inference and strong energy efficiency for supported models. TPUs are powerful in cloud environments, but they are less flexible for non‑ML tasks and may require code that fits their pattern.

FPGAs are reconfigurable chips. You can tailor them to a single workflow, which can yield very low latency and efficient performance. They suit edge devices or custom data paths well. The downside is a steeper learning curve and longer development time, since you write hardware‑like logic rather than just software.

Choosing among them depends on your workload, budget, and timeline. A simple guide:

  • General ML work with large models: GPU.
  • TensorFlow‑based inference at scale in the cloud: TPU (where available).
  • Custom pipelines, ultra‑low latency, or restricted power: FPGA.

In many teams, these tools are used together. CPUs handle control tasks, GPUs train models, and FPGAs or other accelerators run specialized scoring or edge routines. This mix can save time and energy.

Key numbers, not hype: the right accelerator speeds up the job without breaking your budget and timeline.

Key Takeaways

  • GPUs are versatile for large, parallel workloads and broad software ecosystems.
  • TPUs offer fast ML inference and energy efficiency in supported cloud environments.
  • FPGAs provide customizable, low-latency paths for specialized tasks, with longer development time.