Nvidia designs GPU accelerators, networking hardware, and AI computing systems. Its Hopper and Blackwell GPU architectures are the dominant platforms for AI model training and inference globally, used by virtually every major AI lab, cloud provider, and enterprise deploying large-scale AI systems. Nvidia sells both discrete GPUs and complete rack-level systems — the NVL72 — and provides CUDA, the software platform that most AI workloads are written for.

Revenue reached $130.5B in FY2025 and $215B in FY2026, with the data center segment driven almost entirely by AI GPU demand. Data center revenue hit $39.1B in Q1 FY2026, up 73% year-over-year, as Blackwell deployments at hyperscalers and cloud providers continued to accelerate.  The Blackwell architecture introduced the GB200 GPU and the NVL72 rack system, integrating 72 Blackwell GPUs into a single liquid-cooled unit with shared memory and high-bandwidth NVLink connections.

AI inference has grown into a revenue driver comparable in scale to training. In December 2025, Nvidia made its largest-ever deal, acquiring assets from AI chip startup Groq for approximately $20 billion in cash. Jensen Huang stated the plan is to integrate Groq's low-latency processors into the Nvidia AI factory architecture. The result is the Groq 3 LPX inference accelerator, which Nvidia claims delivers 35x higher throughput per megawatt for trillion-parameter AI models compared to the Blackwell NVL72. Nvidia continues to invest in inference optimization software including TensorRT and Triton. The company also announced manufacturing of AI supercomputers in the US in partnership with Foxconn and other contract manufacturers, and the Rubin GPU architecture remains on the roadmap for 2026.