Cloud GPU

Training vs Inference: What's the Difference?

Training is the process of teaching an AI model by exposing it to data — it requires maximum GPU compute and memory. Inference is running a trained model to make predictions — it requires cost efficiency and low latency. According to Signwl data, training-class GPUs average 3-10x more per hour than inference-class GPUs, reflecting their higher performance requirements.

What is Training?

Training (or pre-training) is the computationally intensive process of teaching an AI model from data. The model processes millions or billions of examples, adjusting its internal parameters (weights) to minimise errors.

Training requires GPUs with maximum compute throughput (high TFLOPS), large memory (to hold model weights, gradients, and optimiser states), and high memory bandwidth (to feed data to compute cores fast enough). GPUs like the H100, A100, and MI300X are designed for this.

Training a large language model can take weeks or months on hundreds or thousands of GPUs, costing millions of dollars in compute.

What is Inference?

Inference is running a trained model to generate predictions or outputs — answering questions, generating images, classifying data. It's what happens when you use ChatGPT, image recognition, or any AI-powered product.

Inference requires cost efficiency (you're serving many users, so cost-per-query matters), low latency (users expect fast responses), and sufficient memory to hold the model. GPUs like the T4, L4, and L40S are optimised for inference — they offer good performance at a fraction of the cost of training GPUs.

Fine-Tuning: The Middle Ground

Fine-tuning is a lighter form of training where a pre-trained model is adapted to a specific task using a smaller dataset. It requires more compute than inference but less than full pre-training.

Fine-tuning can often run on mid-tier GPUs like the A100 or L40S rather than requiring flagship H100s. The choice depends on model size and dataset — a 7B model can be fine-tuned on a single A100, while a 70B model may need multiple GPUs.

Frequently Asked Questions

What is the difference between training and inference?

Training teaches a model from data (compute-intensive, takes days-months). Inference runs a trained model to make predictions (cost-sensitive, takes milliseconds). Training uses high-end GPUs; inference uses cost-efficient GPUs.

Which GPU should I use for training?

For large-scale training: H100, H200, B200, or MI300X. For fine-tuning and mid-scale training: A100 80GB. The choice depends on model size, budget, and required training speed.

Which GPU should I use for inference?

For budget inference: T4 or L4. For mid-tier inference: A10G or L40S. For large model serving: A100 or H200. Match the GPU's memory to your model size — the model must fit in VRAM.