Arunangshu Das Blog

You spin up a new model on your laptop. The prototype performs well on small batches… until you add a larger dataset and a deeper network. Now every epoch crawls, the fan howls, and you’re wondering if you should just “throw a GPU at it.”

Here’s the twist: it isn’t “CPU vs GPU” as much as “CPU and GPU”; each plays a different role in a great training pipeline. The CPU is the head chef: it organizes data, handles logic, and keeps the kitchen running. The GPU is the kitchen crew: thousands of hands doing the same motion quickly, matrix multiplies, convolutions, backprop. Get the pairing right, and training flies. Get it wrong, and you burn days on a computer you don’t need.

When is a CPU enough?

• Prototyping tiny models or classical ML on tabular data.
• Data wrangling, augmentation orchestration, and I/O pipelines.
• Inference for lightweight models or latency-tolerant use cases. (Your “head chef” is great at control flow and system tasks.)

When a GPU is essential

• Deep nets with heavy linear algebra (CNNs, transformers) where parallel math dominates.
• Training with frameworks like TensorFlow or PyTorch on images, video, or long sequences.
• Time-to-result matters (hours on a GPU can equal days on a CPU).

A 60-second decision guide

1. Model depth/size: Deeper vision models (e.g., VGG) and modern transformers are GPU-hungry; VGG alone is known for high parameter counts and long training times, demanding strong hardware.
2. Data scale: High-res images, video, or long text sequences → favor GPU
3. Goal: If you’re customizing an existing model, consider transfer learning & other customization techniques to reduce compute needs.
4. Budget & access: Just starting out? Before you invest in hardware, try a managed notebook with a pre-installed GPU. Explore the list of the top 5 essential tools for deep learning beginners.

Bottom line: pair a capable CPU (to feed and coordinate) with the right GPU (to parallelize the math), and you’ll turn training days into training hours, without overbuying compute.

If this perspective was useful and you’d like more deep-dives on AI

Thanks
Arunangshu