AI Accelerators: GPUs, TPUs and Beyond

AI workloads rely on hardware that can perform many operations in parallel. GPUs remain the most versatile starting point, offering strong speed and broad software support. TPUs push tensor math to high throughput in cloud settings. Beyond these, FPGAs, ASICs, and newer edge chips target specific tasks with higher efficiency. The best choice depends on the model size, the data stream, and where the model runs—on a data center, in the cloud, or on a device.

GPUs

GPUs excel at parallel work: matrix multiplies, data movement, and sustained inference. They shine for both training and real-time serving, thanks to mature software stacks like CUDA, cuDNN, and popular frameworks. Many teams start here because the learning curve is lower and the ecosystem is rich.

  • Pros: flexible, widely supported, scalable from a laptop to a GPU cluster.
  • Cons: power hungry, cost grows with scale, memory and thermals can become a bottleneck.

TPUs

TPUs are designed for high-throughput tensor operations and large models. They pair well with TensorFlow and JAX, and they can speed up training and large-batch inference in managed cloud environments. For some workloads, they deliver better efficiency per operation than general-purpose GPUs.

  • Pros: very high throughput, strong tooling, good support for large-scale training.
  • Cons: ecosystem narrower outside TensorFlow/JAX, access often via cloud.

Beyond

Beyond GPUs and TPUs, other accelerators target latency, energy, or custom ops.

  • FPGAs: reconfigurable, good for low latency and bespoke inference, but longer development time.
  • ASICs: best efficiency for a fixed task, but high upfront cost and limited flexibility.
  • Edge and specialized chips: tiny, power-efficient devices for on-device AI, with different software options.

Choosing an accelerator depends on workload, latency targets, and budget. Start by profiling: how fast do you need results? How large is the model, and how much energy can you spend? Map to the ecosystem: which frameworks and tools are supported? Finally, plan for deployment: data center, cloud, or edge.

Key Takeaways

  • GPUs are versatile and widely supported for many AI tasks.
  • TPUs excel in large-scale tensor workloads with strong cloud tooling.
  • Beyond GPUs/TPUs, FPGAs, ASICs, and edge chips offer efficiency gains with tradeoffs in development and flexibility.