Without specialized silicon, neocloud providers would not exist. Accelerated compute is at the core of every neocloud platform, delivered through specialized chipsets, custom AI accelerators, and high-performance networking silicon. These chip manufacturers supply the hardware for AI workloads and collaborate with neocloud providers on software optimization, system architecture, cooling standards, and deployment models for modern AI training and inference.
Different Types of Chipsets
Neocloud providers use specialized accelerators to train and run large models. These accelerators power AI workloads, but they are fundamentally different in design and use case.
- Graphics Processing Units (GPUs) are flexible, general-purpose parallel processors. Originally designed for graphics, they excel at handling a wide range of workloads, from AI training and inference to scientific computing and visualization.
- Tensor Processing Units (TPUs) are specialized AI accelerators designed for deep learning operations, particularly large-matrix math. They deliver exceptional efficiency for large-scale training and inference, but are less flexible and mainly available within the Google ecosystem.
- AI Accelerators/AI Units (AIUs) are used for specialized inference or training for cost-optimized models. They offer better performance per watt for specific workloads. Some examples include AWS Tranium and Inferentia, Intel Gaudi, Groq LPU, and Cerebras WSE.
AMD
AMD is emerging as an important alternative GPU supplier for neocloud providers seeking cost-effective, AI-optimized compute at scale. Its accelerators emphasize high memory capacity and are supported by an open-source software ecosystem that enables community-driven innovation and greater flexibility for operators. Beyond silicon, AMD is actively building strategic partnerships with neocloud providers such as Crusoe, Vultr, and TensorWave, complementing its hardware offerings with support programs, financial incentives, and go-to-market collaboration.
Broadcom
Broadcom plays a foundational role in the neocloud ecosystem by supplying high-performance networking silicon and custom AI chips that enable large-scale, GPU-dense AI infrastructure. Its networking technologies are designed to efficiently interconnect thousands of GPUs within AI factories, supporting the high-bandwidth, low-latency communication required for distributed AI training and inference. In parallel, Broadcom develops custom AI chips for major technology companies, including Google, OpenAI, and Anthropic, reinforcing its position as a critical enabler of scalable, hyperscale-grade AI architectures used by neocloud providers.
Google TPU
Google TPUs are custom application-specific integrated circuits (ASICs) designed for deep learning workloads. TPUs are optimized for matrix multiplication and large-scale model training, delivering strong performance-per-watt and performance-per-dollar for specific AI tasks. Deployed mainly within Google Cloud, TPUs are tightly integrated with Google’s software stack, including TensorFlow and JAX, and are used to train and run large models such as Gemini and other Google AI services.
NVIDIA
NVIDIA is the dominant supplier of GPUs for neocloud platforms, with products like the H100 and GH200 widely used for AI training, inference, and HPC workloads. Its strength is flexibility and ecosystem depth. NVIDIA GPUs support a broad range of workloads and are paired with a mature software stack (CUDA, cuDNN, TensorRT) deeply embedded across cloud, on-premise, and multi-cloud environments. This combination makes NVIDIA GPUs the default choice for experimentation, custom model development, and diverse AI applications across neocloud providers.
