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| NVIDIA Ampere architecture | |
|---|---|
| Name | NVIDIA Ampere architecture |
| Developer | NVIDIA |
| First release | 2020 |
| Predecessor | NVIDIA Turing architecture |
| Successor | NVIDIA Ada Lovelace architecture |
| Architecture | Graphics processing unit |
| Process | TSMC and Samsung 8 nm/7 nm variants |
| Key features | Tensor Cores, Ray Tracing Cores, SM redesign, PCIe 4.0 |
NVIDIA Ampere architecture The Ampere architecture is a GPU microarchitecture developed by NVIDIA that succeeded the NVIDIA Turing architecture and powered products across consumer, professional, and datacenter markets, competing with offerings from AMD and influencing designs by Intel. Announced in 2020 during shifts in the semiconductor industry and deployed in cards such as the NVIDIA GeForce RTX 30 series and the NVIDIA A100, Ampere integrated new Tensor Core designs, second-generation RT Core units, and a reworked streaming multiprocessor to accelerate workloads in gaming, scientific computing, and machine learning. Its launch intersected with supply-chain pressures involving TSMC, Samsung Electronics, and global demand from cloud providers like Amazon Web Services and Microsoft Azure.
Ampere's public unveiling aligned with announcements from NVIDIA executives and partners including Jensen Huang, and drew comparisons to architectures like NVIDIA Volta and competitors such as AMD RDNA 2 and Intel Xe. Market reception connected Ampere to trends tracked by firms like Jon Peddie Research, Mercury Research, and Gartner, influencing procurement by hyperscalers such as Google Cloud and research institutions like Lawrence Livermore National Laboratory and CERN. The architecture targeted diverse segments spanning gaming PCs from vendors like ASUS, MSI, and Gigabyte, workstation markets served by Dell and Lenovo, and supercomputing deployments exemplified by systems at Oak Ridge National Laboratory.
Ampere redesigned the streaming multiprocessor (SM) to increase arithmetic throughput per clock and included third-generation NVIDIA Tensor Core units and second-generation RT Cores to accelerate matrix operations and ray tracing respectively, drawing lineage from NVIDIA Volta innovations and research collaborations with institutions such as Stanford University and MIT. On-die interconnects and memory subsystems used high-bandwidth memory in datacenter SKUs like the NVIDIA A100 and GDDR6/GDDR6X in consumer SKUs such as the GeForce RTX 3080 and GeForce RTX 3090, paralleling memory strategies seen in products from AMD Radeon RX 6000 series and enterprise accelerators from Intel. Ampere introduced a third-generation NVLink and PCIe 4.0 support, echoing system-level designs used by Supermicro and integrated into platforms from HPE and Lenovo for HPC and AI workloads. Chiplets, packaging, and power delivery reflected fabrication partnerships with Samsung Electronics and TSMC and supply strategies monitored by Bloomberg and IEEE Spectrum analysts.
Ampere continued to rely on CUDA as its primary programming model, with enhancements in CUDA Toolkit releases that exposed new intrinsics, mixed-precision primitives, and improved libraries such as cuDNN, cuBLAS, and TensorRT used by developers at companies like OpenAI and research groups at University of California, Berkeley. Frameworks including TensorFlow, PyTorch, and MXNet adapted kernels and autotuning for Ampere’s Tensor Cores and RT Cores, while compilers and toolchains from NVIDIA and partners such as LLVM-based projects enabled optimization. Ecosystem integrations involved orchestration and deployment tools from Kubernetes-oriented vendors, cloud ML platforms like Google Colab and AWS SageMaker, and benchmarking suites used by labs at Stanford and MIT CSAIL.
Independent benchmarks from outlets including AnandTech, TechPowerUp, and Tom's Hardware compared Ampere consumer GPUs to predecessors and rivals, reporting large gains in rasterization, ray tracing, and AI inference throughput, with the NVIDIA A100 showing significant improvements on HPC benchmarks such as LINPACK and MLPerf. Ampere’s mixed-precision performance leveraged Tensor Float 32 (TF32) and BF16 modes to accelerate training and inference in models developed by organizations like DeepMind and OpenAI, affecting results reported by academic groups at Carnegie Mellon University and industry labs at Facebook AI Research. Gaming benchmarks across titles reviewed by Polygon, Eurogamer, and PC Gamer highlighted gains in titles using ray tracing such as Cyberpunk 2077 and Control, while power-constrained mobile and small-form-factor tests by vendors like Razer and ASUS ROG revealed tradeoffs between performance and thermals.
Ampere powered a range of GPUs and systems: consumer GeForce cards (RTX 3070, RTX 3080, RTX 3090), workstation Quadro-branded variants, and datacenter accelerators including the NVIDIA A100 and DGX systems used by enterprises like NVIDIA DGX Station customers and cloud providers including Microsoft Azure and Oracle Cloud. Hardware partners such as EVGA, ZOTAC, PNY, and system integrators including Dell EMC and HPE produced Ampere-based products for gaming, professional visualization, and enterprise AI. Research deployments featured Ampere in supercomputers at facilities like Fugaku-adjacent projects and accelerated computing clusters at Lawrence Berkeley National Laboratory.
Ampere's increased transistor budgets and clock targets required advances in power delivery and cooling; board partners implemented multi-phase VRMs, vapor chambers, and triple-fan designs inspired by thermal engineering practices at companies like Noctua and Cooler Master. Data center implementations emphasized liquid cooling, direct-to-chip cold plates, and facility-level cooling strategies used by hyperscalers Google and Facebook, while OEMs like Dell and Lenovo integrated thermal solutions into rack-mounted systems. Efficiency metrics discussed in whitepapers by NVIDIA and analyses by Uptime Institute compared performance-per-watt against predecessors and competitors, with attention from regulatory and standards bodies such as ENERGY STAR observers.
Ampere was widely discussed across technology media, enterprise analysts at Gartner and Forrester, and academic citations at conferences like NeurIPS and ISCA for its impact on deep learning and HPC, influencing software stacks at companies like NVIDIA partners and research at institutions including MIT and Stanford University. The architecture affected GPU market dynamics, prompting responses from AMD and strategic initiatives from cloud providers including Amazon Web Services and Microsoft Azure, and contributed to debates about supply chains covered by outlets such as The Wall Street Journal and Reuters. Ampere’s role in accelerating AI research, gaming realism, and scientific simulation cemented its presence in contemporary computing discourse across industry and academia.
Category:Graphics processing units