Peter Smith
⬤ DeepSeek ran into serious roadblocks last year while developing its next-generation AI model. The company initially tried training with lower-tier chips from Chinese manufacturers like Huawei, but the hardware couldn't handle the workload. Performance issues with the domestic chips stalled progress and forced the team to rethink their approach.
⬤ Faced with those limitations, DeepSeek brought in Nvidia chips to power the most demanding parts of the training process. The company didn't scrap all its existing hardware—instead, it used Nvidia's GPUs specifically for tasks that needed more computational muscle. While the exact chip models and training scale weren't revealed, the switch clearly solved the performance bottleneck they'd hit with Chinese-made processors.
⬤ Training kicked off last year, and once DeepSeek integrated Nvidia hardware into the workflow, development picked up momentum. Sources close to the project say the company is now gearing up to release the model in the next few weeks, though specific features and capabilities remain under wraps.
⬤ This case shows just how tough it is to train cutting-edge AI when your hardware can't keep up. DeepSeek's pivot illustrates the reality many Chinese AI developers face—balancing what's locally available against what's actually needed to build competitive models. It also makes clear that even with ongoing efforts to develop alternatives, high-performance accelerators like Nvidia's remain essential for serious AI work.
Peter Smith
