GPU

Self-hosting models or fine-tuning requires GPU knowledge. Interviewers ask when GPU beats API and cost tradeoffs.

GPU is a freight train for parallel cargo — overkill for one package, essential for moving a warehouse.

Scenario

Fine-tune 7B model on 1x A100 with QLoRA in 4 hours vs impossible on CPU.

Solution

In Production Agent Engineering, apply GPU to this scenario: Fine-tune 7B model on 1x A100 with QLoRA in 4 hours vs impossible on CPU. Identify the inputs, run the technique, validate the output, and note one thing you would monitor in production.

Do this before moving to the next module — reading alone is not enough.

Open the Code Walkthrough below and run it locally. Change one parameter related to GPU (e.g. model, temperature, top_k, or tool name), observe the difference in output, and write 2–3 sentences explaining what changed.

Highlighted lines show where GPU happens in the code.

1# GPU — minimal example2from openai import OpenAI3
4client = OpenAI()  # create API client5
6# Ask the model to explain this topic7response = client.chat.completions.create(  # core API call for GPU8    model="gpt-4o-mini",9    messages=[10        {"role": "system", "content": "You explain gpu clearly."},11        {"role": "user", "content": f"What is gpu?"},12    ],13    temperature=0,14)15print(response.choices[0].message.content)  # show output for debugging

• Treating GPU as a black box without evaluation
• Ignoring cost and latency in production
• Skipping error handling for gpu

Quick recap — the most important points from this module.