Thanks, Jensen.
Moving to Data Center. Record revenue of $3.3 billion grew 11% sequentially and 71% from a year earlier. Fiscal year revenue of $10.6 billion was up 58%.
Data center growth in the quarter was once again led by our compute products on strong demand for NVIDIA AI. Hyperscale and cloud demand was outstanding, with revenue more than doubling year-on-year. Vertical Industries also posted strong double-digit year-on-year growth led by consumer Internet companies. The flagship NVIDIA A100 GPU continue to drive strong growth. Inference-focused revenue more than tripled year-on-year.
Accelerating inference growth has been enabled by widespread adoption of our Triton and France server software, which helps customers deliver fast and scalable AI in production. Data center compute demand was driven by continued deployment of our Ampere architecture-based product for fast-growing AI workloads such as natural language processing and deep learning recommendation systems as well as cloud executing. For example, Block Inc., a global leader in payment, uses conversational AI in its Square Assistant to schedule appointments with customers. These AI models are trained on video GPUs in AWS and perform inference 10x faster on the AWS GP service and on our CPUs. Social media company Snap used NVIDIA GPUs and Merlin deep recommendator software to improve inference cost efficiency by 50% and decrease latency to 2x.
For the third year in a row, industry benchmarks show that NVIDIA AI continues to lead the industry in performance. Along with partners like Microsoft Azure, NVIDIA such records in the latest benchmarks for AI training across 8 popular AI workloads, including computer vision, natural language processing, recommendation systems, reinforcement learning and detection. NVIDIA AI was the only platform to make submissions across all benchmarks and use cases, demonstrating versatility as well as our performance. The numbers show performance gains on our A100 GPUs of over 5x in just 2 months, thanks to continuous innovations across the full stack in AI algorithms, optimization tools and system software. Over the past 3 years, they saw performance gains of over 20x powered by advances we have made across our full stack offering GPUs, networks, systems and software. The leading performance of NVIDIA AI is sought after by some of the world's most technically advanced companies. Meta Platforms unveils its new AI supercomputer research, SuperCluster, with over 6,000 A100 GPUs moved to an NVIDIA — Meta's early benchmarks showed its system can train large natural language processing models 3x faster and run computer vision jobs 20x faster than the prior system. In a second phase later this year, the system will expand to 16,000 GPUs that Meta believes will deliver 5x of mixed precision AI performance. In addition to performance at scale, Meta cited extreme reliability, security, privacy and flexibility to handle a wide range of AI models as its key criteria for the system.
We continue to broaden the reach and ease the adoption of NVIDIA AI into vertical industries. Our ecosystem of NVIDIA-certified systems expanded with Cisco and Hitachi — which joined Dell, HewlettPackard Enterprise, Insper, Lenovo and Supermicro, among other sever manufacturers. We released version 1.1 of our NVIDIA AI Enterprise software, allowing enterprises to accelerated annual workloads on VMware, on mainstream IT infrastructure as well. And we expanded the number of system integrators qualified for NVIDIA AI Enterprise. Forrester Research in its evaluation of Enterprise AI infrastructure providers recognized NVIDIA in the top category of leaders. An example of a partner that's helping to expand our reach into enterprise IT is Deloitte, a leading global consulting firm, which has built its center for AI computing on NVIDIA DGX Superpod.
At CES, we extended our collaboration to AV development, leveraging our own robust AI infrastructure and Deloitte's team of 5,500 system integration developers and 2,000 data scientists to architect solutions for truly intelligent transportation. Our networking products posted strong sequential and year-over-year growth, driven by exceptional demand across use cases ranging from computing, supercomputing and enterprise to storage. adopters-led growth driven by adoption of our next-generation products and higher-speed deployments. While revenue was gated by supply, we anticipate improving capacity in coming quarters, which should allow us to serve with significant customer demands we're seeing. Across the board, we are excited about the traction we are seeing with our new software business models, including NVIDIA AI, NVIDIA Omniverse and NVIDIA DRIVE. We are still early in the software revenue ramp. Our pipelines are building as customers across the industry seek to accelerate their pace of adoption and innovation with NVIDIA. Now let me turn it back over to Jensen for some comments on Arm.