How to adapt your IT infrastructure for AI
Artificial intelligence (AI) is transforming businesses of all sizes. But adapting existing IT infrastructure to support AI workloads can be challenging.
One key challenge is accessing hardware with the compute power to handle AI. Another is that AI applications often have different requirements for training and inference. Training requires high compute power, while inference can be done on less powerful hardware.
Some firms retrofit existing servers to handle AI training. This can be a good option for getting started with AI, but it’s important to consider the risks and considerations. For example, running AI workloads on existing hardware clusters can introduce performance and reliability issues.
Another option is to invest in new hardware specifically designed for AI. This can be a more expensive option, but it can provide better performance and reliability.
Here are some tips for adapting your IT infrastructure for AI:
- Assess your needs. What kind of AI workloads do you need to support? How much compute power do you need?
- Consider your budget. How much are you willing to spend on new hardware?
- Evaluate your options. There are a variety of hardware options available for AI. Consider your needs and budget when making a decision.
- Plan for the future. AI is rapidly evolving. Make sure your hardware is scalable and flexible enough to meet your future needs.
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