Would you rent a GPU to run AI models?

Hey everyone 👋

Curious how folks here are handling compute for AI workloads in practice.

If you’re working with LLMs, vision models, speech pipelines, or even smaller experiments, you’ve probably hit the compute wall at some point. Buying GPUs is expensive and not always easy to scale, while managed APIs can limit flexibility and control.

So here’s the question:

Would you rent a GPU (bare metal or virtual) to run your own AI models?

At Qubrid AI, we’ve been seeing more teams move toward renting GPU infrastructure to run open models and production inference workloads, and it made us curious how common that approach really is across the community.

Would love to hear your perspective:

• What kind of workloads are you running today? (training, fine-tuning, inference, agents, etc.)
• Do you prefer owning hardware vs renting vs APIs?
• What matters most to you: cost, performance, privacy, control, or ease of use?
• If you’ve rented GPUs before, what worked well and what didn’t?
• If you don’t rent GPUs today, what’s the main blocker?

Also curious what your ideal GPU setup looks like right now.

Looking forward to hearing how everyone here is approaching this 👇