The short version
NVIDIA combines general-purpose GPUs with CUDA, TensorRT, networking, and a broad server ecosystem. Cerebras centers on the WSE wafer-scale processor and CS systems, trying to keep a large amount of compute and communication inside one enormous chip. Google TPU is an ASIC designed by Google for machine-learning matrix workloads and offered through Cloud TPU and Pod configurations for training, fine-tuning, and inference.
Architecture differences
NVIDIA GPUs keep broad programmability while Tensor Cores accelerate deep-learning matrix math. They also run graphics, HPC, data processing, and custom CUDA kernels. Cerebras WSE trades the normal small-die composition model for wafer-scale on-chip communication and SRAM. Google TPU is organized around matrix multiply units, vector units, inter-chip interconnect, and a compiler stack built for neural-network execution.
Software ecosystem differences
NVIDIA's strongest advantage is the CUDA ecosystem: frameworks, operator libraries, inference engines, debugging tools, cluster software, and third-party experience are all mature. TPUs are commonly used through JAX, PyTorch/XLA, TensorFlow, and Google Cloud tooling, which fits teams already optimizing around Google Cloud or XLA. Cerebras provides its own software stack and model execution path, with an emphasis on simplifying some large-model partitioning and distributed scheduling work.
When each path fits
If a team needs the broadest software compatibility, flexible procurement, and mature ecosystem support, NVIDIA GPUs are usually the lowest-risk option. If the workload fits wafer-scale execution and the priority is on-chip bandwidth, latency, or simpler large-model parallelism, Cerebras is worth evaluating. If the team trains or serves large models on Google Cloud and can work within TPU/XLA deployment patterns, Google TPU can offer strong system-level efficiency. The real decision depends on model shape, batch size, memory pressure, network communication, engineering experience, and cost per useful token or training step.