Building Hardware-Agnostic AI Pipelines

With Qualcomm's acquisition of Modular and OpenAI's custom chip, the era of hardware lock-in is ending—but only if you design your pipeline to be portable. The key insight is that inference performance depends less on the chip and more on the software stack that sits between your model and the hardware. Modular's approach uses a compiler-based abstraction that converts model graphs into optimized code for any target. To build hardware-agnostic pipelines today, start by using frameworks like ONNX or TensorRT that support multiple backends. Avoid writing custom CUDA kernels unless absolutely necessary, because they tie you to Nvidia. Instead, use high-level APIs like PyTorch's torch.compile or TensorFlow's XLA, which can target different accelerators. Next, containerize your inference server with Docker so you can deploy on any cloud or edge device. Use Kubernetes for orchestration, which abstracts the underlying hardware from your application logic. Finally, benchmark your model on at least two different hardware types (e.g., Nvidia A100 and AMD MI300) to verify performance portability. The goal is to make your AI stack as easy to move as a web app—write once, deploy anywhere. This is exactly what Modular enabled, and now Qualcomm will push it further. If you start now, you'll be ready when the next hardware shift happens.