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 widely used for AI, graphics, and scientific computing. An AI accelerator is a broader category that includes GPUs plus purpose-built chips (like TPUs or AWS Inferentia/Trainium) designed specifically to run AI operations (such as matrix multiplications) more efficiently for training or inference.

Question 2

When should I use an AI Accelerator?

Accepted Answer

Use an AI accelerator when CPU performance is too slow or too expensive for your ML workload—common triggers include large deep learning models, strict latency targets (real-time inference), high throughput needs (batch inference), or long training times. If your model is small, runs infrequently, or is dominated by data loading/ETL rather than math, a CPU may be simpler and cheaper.

Question 3

How much does an AI Accelerator cost?

Accepted Answer

Costs vary mainly by accelerator type (GPU vs custom chip), size (memory/compute), region, and how long you run it. Pricing is typically per-hour (or per-second) for accelerator-backed VM instances, plus storage and data transfer. Key cost drivers include utilization (idle time is expensive), model size (memory needs), precision mode (FP16/BF16/INT8), and whether you can use spot/preemptible capacity or reserved/committed use discounts.

AI Accelerator

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