Question 1

What's the difference between an AI accelerator and a GPU?

Accepted Answer

A GPU is a general-purpose parallel processor that’s great for many workloads, including graphics and AI. An AI accelerator is specialized hardware designed specifically for common AI operations (like matrix multiplication and tensor operations). Some accelerators are GPUs (because modern GPUs include AI-focused tensor cores), while others are custom chips (like TPUs or AWS Inferentia/Trainium) built primarily for training or inference efficiency.

Question 2

When should I use an AI accelerator?

Accepted Answer

Use an AI accelerator when AI workloads are a performance or cost bottleneck—especially for (1) training large models faster, (2) serving real-time inference with low latency, (3) running high-throughput batch inference, or (4) reducing cost per prediction. If your model is small, traffic is low, or CPU performance is already sufficient, you may not need an accelerator.

Question 3

How much does an AI accelerator cost?

Accepted Answer

Costs vary by provider, accelerator type (GPU vs TPU vs custom chip), region, and whether you’re training or doing inference. Pricing is typically per hour (VM/instance) or per accelerator device, plus storage and networking. Key cost drivers include: accelerator-hours, memory size (HBM/VRAM), interconnect (NVLink/cluster networking), utilization (idle time is expensive), and software efficiency (batching, quantization, compilation). Savings often come from higher throughput per dollar and better utilization rather than a lower hourly rate.

AI Accelerator

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