For AI startups, GPUs are both an engine for innovation and a major expense. They’re the key to training models faster, running complex inferences, and staying ahead of the competition, but they can also drain a startup’s resources if not used strategically.
So, how can early-stage AI companies balance performance, innovation, and cost efficiency?
During our recent panel discussion, we explored this challenge and shared practical strategies for getting the most out of GPUs without overspending 👇
1. Start with pre-trained models
Training AI models from scratch is expensive and time-consuming. Instead, start with pre-trained open models such as Llama and its many variants.
You can fine-tune these models for your specific needs at a fraction of the cost, significantly reducing GPU usage. It’s also worth considering distilled models and CPU-efficient frameworks that let you run lighter workloads on cheaper hardware or even CPUs.
2. Leverage affordable GPU providers
Not all GPU providers charge the same rates. Transparent pricing and no hidden fees can make a significant difference to your bottom line.
At Civo, we offer transparent, affordable GPU pricing designed with startups in mind. You don’t always need the newest GPU model to get great results; options like L40s or previous-generation A100s can still deliver excellent performance for less, helping you stretch your budget without compromising on capability.
👉 Get started with Civo GPUs by clicking here!
We looked into the trends and challenges of AI adoption in our latest whitepaper to uncover how the cost and complexity of essential infrastructure, like GPUs, remain significant barriers for many organizations.
”We believe that access to cutting-edge technology should not be a barrier to innovation, and that every company should have the opportunity to leverage advanced and secure cloud computing technologies” - Josh Mesout, Chief Innovation Officer at Civo
3. Start small, scale smart
GPUs come in a variety of models (e.g., B200, A100, L40S and H100), with newer hardware carrying a higher cost. Save significantly by prototyping on smaller datasets and using older-generation GPUs, then scaling up to more powerful hardware as your project matures.
| Model | Status | From price | On-demand price |
|---|---|---|---|
| A100 40GB | In Stock now | $0.69 / GPU·h | $1.09 / GPU·h |
| A100 80GB | In Stock now | $1.39 / GPU·h | $1.79 / GPU·h |
| L40S 40GB | In Stock now | $0.89 / GPU·h | $1.29 / GPU·h |
| H100 PCIe | In Stock now | $1.99 / GPU·h | $2.49 / GPU·h |
| H100 SXM | In Stock now | $2.49 / GPU·h | $2.99 / GPU·h |
| H200 SXM | In Stock now | $2.99 / GPU·h | $3.49 / GPU·h |
| B200 | In Stock now | $22.32 / GPU·h | Price on request |
Prices accurate at the date of publication: August 2025. For more information on pricing and the savings you can make, see our pricing page here.
4. Consider turnkey AI solutions
If rapid productivity with AI is your priority, turnkey solutions can be a game-changer. By providing a pre-configured and ready-to-use system, turnkey solutions directly enable businesses to achieve rapid productivity without the need for extensive setup or technical expertise.
relaxAI offers seamless one-to-one compatibility with the OpenAI API. Simply plug in your API key and base URL to start coding right away, with minimal migration effort. Plus, relaxAI includes a secure web interface for uploading your company’s data, which is stored exclusively in UK-based data centers and governed by UK law, making it an ideal choice for teams focused on data sovereignty and privacy.
If you want to learn more about the relaxAI API, visit our website, and to find detailed documentation to get started on your projects, click here.
5. Use quantization to reduce GPU load
Quantization reduces the precision of your model’s weights, cutting down the number of computationally expensive tensor multiplications needed during training or inference. Civo's GPU instances are optimized to take full advantage of quantized models, allowing you to run AI workloads more efficiently and cost-effectively on our platform.
By leveraging quantization, you can significantly reduce the computational resources required, making our more affordable GPU options, such as the L40S or A100. This means you can achieve faster and more cost-effective model execution without compromising on performance.
To learn more about Civo’s GPU offerings, check out our resources here.
Summary
Before choosing a GPU strategy, ask yourself what matters most to your business:
- Do you want the fastest, most turnkey path to AI productivity?
- Or do you want full control over infrastructure to optimize GPU use?
For many startups, the answer is a blend of both: leveraging cost-efficient GPU access for flexibility, while using turnkey platforms like relaxAI to move fast and stay productive. This hybrid approach offers the best of both worlds, and Civo provides the tools to help you get started on either path.
Ready to dive deeper into the world of GPUs and AI? Explore these resources to learn more about how Civo is helping to shape the future of the GPU landscape:
