Emerging cloud providers (“NeoClouds”) are sticking exclusively with NVIDIA, despite alternatives from AMD. The perceived performance risk is too high, as customers demand state-of-the-art inference speed and providers can't risk a multi-billion dollar investment on a non-NVIDIA stack that might offer lower throughput.

New AI models are designed to perform well on available, dominant hardware like NVIDIA's GPUs. This creates a self-reinforcing cycle where the incumbent hardware dictates which model architectures succeed, making it difficult for superior but incompatible chip designs to gain traction.

While NVIDIA's CUDA software provides a powerful lock-in for AI training, its advantage is much weaker in the rapidly growing inference market. New platforms are demonstrating that developers can and will adopt alternative software stacks for deployment, challenging the notion of an insurmountable software moat.

The high cost of GPUs means any inefficiency during model training is extremely expensive. This economic reality justifies building specialized, AI-focused infrastructure with features like advanced observability and optimized storage to maximize GPU utilization and prevent costly delays from failures or slowdowns.

Hardware vendors like NVIDIA (CUDA) and AMD create fragmented, proprietary software stacks that lock developers in. Modular builds a replacement layer that enables AI models to run consistently across different hardware, giving enterprises choice and flexibility without rewriting code.

A key challenge with cloud-deployed agents is their lack of cost discipline; they often keep expensive GPU instances running unnecessarily. This is fueling a trend towards using powerful, one-time-purchase local hardware like the DGX Spark for agent development and deployment.

The model's unified architecture eliminates handoffs between separate speech recognition, language model, and text-to-speech components, achieving a low 450ms latency. However, this monolithic design prevents users from swapping in specialized or superior components, a key advantage of older, cascaded systems.

AI performance engineer Chris Fregley warns that developing on local machines or even consumer-grade GPUs is a waste of time. Critical differences in hardware, memory bandwidth, and drivers mean that accurate profiling and optimization can only be done on the exact production systems, like NVIDIA's Blackwell or Hopper GPUs.

Top AI companies like Meta, Microsoft, and OpenAI are so desperate for compute that they willingly manage systems from both NVIDIA and AMD. This urgent need for capacity overrides the significant operational complexity of writing software that works across different hardware vendors.