But an AI accelerator is only as useful as the software that lets it run a model, and for AMD's Instinct GPUs, ROCm is that software. If CUDA is the reason NVIDIA's hardware advantage has proven so durable, ROCm is AMD's answer to the same problem: how do you get PyTorch, vLLM, and a few hundred thousand developers to treat your silicon as a first-class citizen rather than an afterthought.

Recently, NVIDIA introduced a new architecture, called NVHBM, which pulls the memory controller out of the XPU chip and puts it inside the HBM stack's own base die. Amazon's Annapurna Labs is already on board for Trainium4.

On July 15, 2026, the Union Cabinet approved Semicon 2.0, the second phase of India's semiconductor policy, with a fiscal outlay of ₹1,27,500 crore (roughly $13.3 billion). While the numbers look good, I believe it’d be good to look at what Semicon 2.0 actually proposes, how it builds on its predecessor, and where there are gaps, still.

If compute is the engine of AI infrastructure, the network is the nervous system. It's time to turn to the two standards that carry data between servers and racks: InfiniBand and Ethernet.

In mid-July 2026, a Beijing-based startup most people outside AI circles had never heard of released what it calls the largest “open-weight” AI model in the world, meaning the trained model itself, not just access to it through an app or API, is available for anyone to download and run, and for a few days it became the biggest story in the industry.