How To Deal With AI Parts Shortages In Your Business

Because of the astonishing rise of AI computing, parts shortages are becoming more acute. They are particularly bad for things like Nvidia’s H-Series chips, Nvidia’s B200 series, other GPUs, DRAM and high bandwidth memory. These parts are all required by AI data centres and are currently in massive demand. In early 2026, data centres are responsible for the consumption of more than 70% of global memory production. This means that 70% of all chips being created today for memory purposes are being shipped to massive data centres to be used by many of the biggest companies in the world to train AI models. This means that lead times on many products are actually quite long and can run all the way into 2027.

So, if you’re a company that relies on AI-related parts and you’re experiencing shortages, what can you do? 

Optimize your existing hardware

The first, and perhaps most common, option is to optimize your existing hardware. Of course, this route has limitations, but it can get you out of a bind for a few months. For example, you could use things like dynamic scheduling or orchestra tools. Some companies also use GPU sharing which is useful. There are also other techniques like offline queuing, time slicing, back matching, and quantization. If you implement optimizations efficiently and effectively, you can expect gains of up to 50%. These types of improvements can give you as much as a year to procure new pieces of equipment. 

Shift to cloud computing strategies

The other option is to go to where the chip is actually being used in data centers that provide cloud-based services (Google Cloud, Azure, and AWS) as well as specialized options like Lambda Labs and Hyperstack. All allow you to rent hardware that you need as you grow and try to bring additional equipment in-house. For example, use a cloud-only setup or multiple cloud options, or you can have a hybrid model where you do some of the workloads in-house and then outsource whatever you can’t do in-house to a third-party provider. 

Look for alternative hardware vendors

Many companies in this situation look for the best server parts wholesale supplier in their niche. This option is extremely popular right now as many companies look to diversify beyond NVIDIA. The obvious options remaining are AMD and Intel with Google producing a series of TPUs which are good for tensor flow based training. AMD has comparable components that can work well in a commercial setting depending on the type of tasks you want to complete. For example, AMD is improving its ROCm ecosystem to make it an alternative to NVIDIA’s current dominance. 

Prioritize efficient AI workloads

The last thing you might want to try is changing the type of AI workloads that you demand from your cloud operations. We suggest auditing your AI pipeline and using model compression and quantization so you can reduce memory requirements across the board. Also, if you are able to shift non-critical training to off-peak or quiet periods, then this can help. Leveraging lower priority hardware and using different times of the day is a reliable approach to cut demands on existing facilities.