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Nvidia Tesla

The NVIDIA Tesla was a product line of graphics processing units (GPUs) developed by Corporation, specifically engineered for (HPC), (AI), data analytics, and professional visualization workloads in data centers and servers, rather than consumer gaming or display graphics. Launched in 2007, the Tesla series leveraged NVIDIA's platform to accelerate compute-intensive tasks, offering significantly higher performance than contemporary CPUs at a fraction of the power consumption—up to 10 times the throughput for certain applications. The brand originated with the Tesla C870, a dual-slot PCIe card based on the , which introduced general-purpose GPU () computing to mainstream scientific and applications by enabling without video output capabilities. Over its lifespan, the line evolved through multiple GPU architectures, including Fermi (e.g., Tesla C2050/C2070 with up to 448 cores), Kepler (e.g., Tesla K40 with 12 GB GDDR5 memory and 1.43 teraFLOPS of double-precision floating-point performance), , , (e.g., Tesla V100 with 640 Tensor Cores for ), and Turing (e.g., Tesla T4 with 16 GB GDDR6 and optimized capabilities). These GPUs were designed for server environments, supporting features like for error correction in mission-critical computations and integration with software for multi-user scenarios. Key innovations in the Tesla lineup included the introduction of Tensor Cores in the Volta-based V100, which accelerated matrix operations essential for and by up to 47 times compared to prior CPU-based systems for certain tasks, and the adoption of high-bandwidth (HBM2) for faster throughput in HPC simulations. The series powered breakthroughs in fields such as climate modeling, , and , with products like the Tesla K80 delivering dual-GPU configurations for up to 24 GB of and 2.91 teraFLOPS of double-precision performance. By 2016, Tesla GPUs were integral to supercomputers topping the list, demonstrating their role in scaling computational power for exascale research. In 2020, discontinued the Tesla branding to avoid confusion with , the manufacturer, transitioning its data center GPU portfolio to the unified "NVIDIA Data Center GPUs" nomenclature starting with the architecture (e.g., A100). Despite the , legacy Tesla products like the V100 and T4 continued to receive driver support into 2025 for ongoing deployments in inference and . The Tesla era solidified 's dominance in accelerated , paving the way for modern infrastructure and contributing to the company's valuation surge in the .

History

Origins and Launch

The inception of the Nvidia Tesla product line stemmed from the growing interest in general-purpose computing on graphics processing units (GPGPU) during the mid-2000s, as researchers sought to leverage the parallel processing power of GPUs for non-graphics workloads beyond traditional CPU limitations. In November 2006, Nvidia introduced the Tesla microarchitecture with the GeForce 8800 GPU based on the G80 design, which unified vertex and pixel processing into a scalable array of streaming multiprocessors, enabling more flexible parallel computation. Concurrently, Nvidia launched the Compute Unified Device Architecture (CUDA) programming model, a C/C++-like extension that simplified GPGPU development by allowing developers to write code for GPUs without relying on graphics APIs, thus addressing the inefficiencies of CPU-based serial processing for data-parallel tasks like simulations and scientific modeling. Building on this foundation, officially launched the product line in June 2007 as a dedicated series of compute-focused GPUs, stripping away display outputs to prioritize (HPC) and scientific applications in environments. The initial offerings included the Tesla C870, a single-slot card; the Tesla D870, a deskside unit housing two C870 GPUs; and the Tesla S870, a rack-mountable with four GPUs, all based on the G80 manufactured on a . These products delivered peak single-precision floating-point performance of 518 GFLOPS for the C870 and approximately 1.0 TFLOPS for the D870, providing a significant leap for parallel workloads while consuming up to 170W per GPU in the C870 model. Priced from $1,499 for the C870 to $12,000 for the S870, they targeted enterprise and research users seeking scalable compute clusters. This launch represented Nvidia's strategic pivot from graphics-centric hardware to programmable platforms, capitalizing on to unlock GPU potential for HPC domains where CPUs struggled with massive . Early demonstrations at events like Supercomputing 2007 highlighted Tesla's integration into clusters, fostering initial partnerships with research institutions for applications in and fluid simulations, including collaborations with national laboratories to accelerate scientific discovery.

Evolution Through Architectures

The Tesla product line evolved significantly through successive GPU architectures, beginning with the introduction of the Fermi architecture in 2010, which marked a pivotal shift toward robust support for scientific computing workloads. The Fermi-based Tesla C2050 and C2070 accelerators, released in March 2010, incorporated error-correcting code ( to enhance data reliability in environments and provided dedicated hardware support for double-precision floating-point operations, achieving up to 0.515 teraflops (TFLOPS) in double precision on the C2070 model. These features addressed key limitations in prior generations, enabling more accurate simulations in fields requiring . The Kepler architecture, launched in 2012, further optimized through redesigned streaming multiprocessor units known as SMX, which improved instruction throughput and reduced power consumption compared to Fermi. The Tesla K20, released in November 2012, delivered 1.17 TFLOPS of double-precision , representing a substantial increase over Fermi's capabilities while maintaining a 225-watt (TDP). This architecture's emphasis on balanced compute efficiency culminated in the Tesla K80 in November 2014, a dual-GPU design that combined two Kepler GK210 dies to provide up to 2.91 TFLOPS of double precision aggregate , facilitating scalable multi-GPU configurations in dense environments. By 2015, the architecture prioritized power efficiency even more aggressively, achieving nearly twice the of Kepler through refined memory hierarchies and techniques. The M40, launched in November 2015, exemplified this focus with a 250-watt TDP and 7 TFLOPS of single-precision performance, while the dual-GPU M60, also released that year, targeted virtualized data center applications with enhanced multi-user support. The Pascal architecture in 2016 introduced high-bandwidth interconnects like , enabling faster GPU-to-GPU communication for large-scale systems. The Tesla P100, released in June 2016, achieved 5.3 TFLOPS of double-precision performance and debuted high-bandwidth memory (HBM2) integration, boosting overall system throughput for demanding workloads. Volta, arriving in 2017, integrated specialized Tensor Cores to accelerate matrix operations critical for , representing a major architectural pivot toward mixed-precision computing. The Tesla V100, launched in May 2017, featured 640 Tensor Cores and delivered up to 125 TFLOPS of Tensor performance, solidifying the Tesla line's role in emerging AI infrastructures while maintaining strong double-precision capabilities at 7.8 TFLOPS. Following the V100, Nvidia phased out the Tesla branding around 2018, transitioning subsequent GPUs like the 2020 A100 under a unified "NVIDIA " lineup to streamline product nomenclature and avoid market confusion. This rebranding concluded the Tesla era, which had spanned nearly a decade of architectural innovations driving advancements.

Technical Architecture

Core Design Principles

NVIDIA Tesla GPUs are engineered with a compute-centric optimized for environments, featuring systems that rely on chassis airflow rather than active fans to dissipate heat efficiently in dense rack configurations. These GPUs adopt rack-mountable designs, such as PCIe cards or modules, which conform to standards like NVIDIA's 3.0, enabling seamless integration into 1U or 2U racks without the need for dedicated cooling infrastructure. Unlike consumer-oriented cards, Tesla GPUs omit video outputs entirely, as they are dedicated to non-graphical compute tasks, eliminating unnecessary display hardware to reduce power consumption and board complexity. The unified in Tesla GPUs integrates high-bandwidth graphics directly with the processor, allowing seamless access to large datasets for parallel computations without the distinct separation found in traditional CPU-GPU systems. Early models, such as the Tesla C870 based on the G80 , utilized GDDR3 connected via a 384-bit interface to achieve bandwidths up to 76.8 GB/s, prioritizing throughput for scientific simulations over latency-sensitive rendering. Over successive generations, this evolved to incorporate advanced technologies, culminating in HBM2 in the Pascal-based Tesla P100 and Volta-based V100, which provide up to 900 GB/s of through stacked DRAM dies and wider interfaces, enhancing performance in memory-intensive workloads like . Scalability is a of Tesla design, with support for high-speed interconnects like enabling multi-GPU clustering to form cohesive compute nodes with aggregate bandwidth exceeding 300 GB/s bidirectional across multiple links, far surpassing standard PCIe limitations for inter-GPU data transfer. This allows configurations of up to eight GPUs in a server, as seen in DGX systems, where facilitates between devices for distributed training and simulations. Complementing this, PCIe integration—typically Gen3 x16—ensures broad compatibility, allowing Tesla GPUs to slot into standard rack servers from vendors like and HPE without custom modifications. The programming model for Tesla GPUs centers on , NVIDIA's platform, which organizes execution into thousands of lightweight threads grouped into warps for SIMD-style processing on arrays of CUDA cores. This enables massive parallelism, with architectures like Fermi featuring up to 512 cores per GPU and later designs scaling to over 5,000 cores, optimized for executing the same instruction across multiple data elements to accelerate vectorized operations in fields like . Developers leverage 's hierarchical model—threads, blocks, and grids—to map workloads efficiently, ensuring high on the GPU's SIMD units for throughput-oriented tasks. To bolster reliability in mission-critical deployments, Tesla GPUs incorporate error-correcting code (ECC) memory starting with the Fermi architecture, which protects DRAM against single-bit errors and detects multi-bit faults, safeguarding data integrity during long-running computations. This feature reserves approximately 12.5% of memory capacity for parity bits but is essential for scientific applications where bit flips could invalidate results, as demonstrated in large-scale HPC clusters running climate modeling or genomics. Subsequent architectures, including Kepler and beyond, extended ECC to caches and registers, further enhancing fault tolerance without compromising peak performance.

Compute and Memory Features

The compute of NVIDIA Tesla GPUs revolves around Streaming Multiprocessors (SMs), which execute parallel workloads through arrays of cores optimized for general-purpose computing. In the Kepler , as exemplified by the Tesla K20, there are 13 SMs, each containing 192 cores for a total of 2496 cores, enabling efficient handling of tasks such as scientific simulations. This design evolved significantly in subsequent generations; the Volta-based Tesla V100 features 80 SMs, with each SM incorporating 64 cores and additional specialized units, reflecting a shift toward higher core counts and integrated accelerators for and (HPC) applications. Tesla GPUs provide robust support for floating-point precision, including dedicated units for single-precision (FP32) and double-precision (FP64) operations compliant with the standard, ensuring accuracy in numerical computations critical for scientific and engineering workloads. Early models like the Kepler Tesla K20 deliver 1.17 TFLOPS of FP64 performance, suitable for double-precision intensive tasks in supercomputing. By the Volta era, the Tesla V100 achieves 7.8 TFLOPS in FP64, a substantial improvement that balances precision with throughput for HPC simulations requiring . The in Tesla GPUs is engineered for high-bandwidth access and low-latency data movement, featuring per-SM and L1 caches alongside a unified cache shared across all . In earlier architectures like Kepler, and L1 cache are distinct, with up to 48 KB of configurable per SM to facilitate fast data sharing among threads. The architecture unifies these into a 128 KB configurable block per SM for L1 cache and , enhancing efficiency for irregular access patterns in and simulations. The cache, at 6 MB in the V100, supports coherent access across , while global exemplifies the hierarchy's scale—for instance, the V100's HBM2 delivers 900 GB/s, enabling rapid data transfer for memory-bound parallel algorithms. Starting with the architecture, Tesla GPUs incorporate Tensor Cores within each SM to accelerate matrix multiply-accumulate operations essential for , processing FP16 inputs with FP32 accumulation for mixed-precision computing. Each Tensor Core in the V100 performs 4x4x4 matrix operations per clock cycle, yielding up to 125 TFLOPS of FP16 throughput and delivering up to 12x higher performance in training compared to the prior Pascal generation's FP32 capabilities. This specialization allows Tesla GPUs to handle the tensor operations prevalent in training and inference far more efficiently than traditional cores. Power and thermal management in Tesla GPUs employ dynamic techniques to optimize performance within thermal limits, with thermal design power (TDP) ratings evolving to support denser compute. Early Kepler models like the K20 operate at 225 W TDP, balancing efficiency for server deployments. The V100 escalates to 300 W in maximum performance mode, leveraging GPU Boost to dynamically increase clock speeds under favorable thermal conditions, thereby sustaining peak throughput for sustained HPC workloads without exceeding power envelopes.

Applications

High-Performance Computing

Nvidia Tesla GPUs have significantly accelerated scientific simulations in (HPC), particularly in fields requiring double-precision floating-point computations for accuracy in modeling complex physical phenomena. In , Tesla accelerators enable faster simulations of atomic interactions by leveraging their high double-precision performance, as demonstrated in ports of codes like , , and NAMD to , achieving up to 15x speedups in benchmarks such as Cellulose_NPT on Tesla P100 GPUs. For climate modeling, Tesla GPUs provided an 80x speedup in weather prediction tasks at the , handling large-scale atmospheric data with ECC-protected double-precision arithmetic to maintain reliability. Similarly, in (CFD), these GPUs support simulations of fluid flows, such as blood circulation or oil recovery processes, by processing vast datasets in double precision on their onboard memory. Tesla GPUs integrate seamlessly with Message Passing Interface (MPI) standards for distributed cluster computing, enabling scalable parallel execution across multiple nodes in HPC environments. This compatibility, supported through CUDA-aware MPI implementations like MVAPICH2 and , allows direct GPU memory transfers via GPUDirect, reducing latency in multi-GPU clusters. During 2013-2015, several supercomputers featured Tesla K20 and K40 GPUs based on the Kepler architecture, powering energy-efficient systems like Eurora, which topped the list in June 2013 with NVIDIA Tesla K20 accelerators for . Other notable entries included TSUBAME-KFC in November 2013, utilizing Kepler GPUs for high-performance workloads. A prominent example is the supercomputer, deployed in 2012 at , which incorporated 18,688 NVIDIA Tesla K20X GPUs alongside CPUs to achieve a peak performance of over 20 petaFLOPS, marking it as the world's fastest system at the time for open scientific research. This hybrid architecture accelerated diverse HPC tasks, from to , by offloading parallel computations to the GPUs. The primary benefits of Tesla GPUs in HPC include 10-100x speedups over traditional CPU-based systems for parallelizable tasks, transforming hours-long computations into minutes while consuming less power. These gains are facilitated by optimized libraries such as cuBLAS for linear algebra operations and cuFFT for fast Fourier transforms, which exploit the GPUs' capabilities in double-precision environments. To address scalability challenges in large clusters, early Tesla systems relied on high-speed interconnect precursors like and Cray's network, enabling efficient communication among thousands of GPUs as seen in Titan's deployment.

Artificial Intelligence and Machine Learning

The adoption of NVIDIA Tesla GPUs in and accelerated significantly from the mid-2010s, driven by enhancements to the CUDA ecosystem that optimized workflows. A key milestone was the release of the cuDNN library in 2014, which provided GPU-accelerated primitives specifically for convolutional neural networks, enabling faster training and inference in applications by leveraging for operations like convolution and pooling. This library integrated seamlessly with emerging frameworks, fostering broader use of Tesla GPUs in AI research and development. Support for major deep learning frameworks further solidified Tesla's role in AI, with gaining native GPU integration upon its initial release in late 2015, allowing developers to accelerate model training via . Similarly, , released in early 2017 but with support developed in 2016, provided dynamic computation graphs optimized for Tesla hardware, promoting rapid prototyping and experimentation in . These integrations reduced computational bottlenecks, making Tesla GPUs essential for scaling training. The introduction of Tensor Cores in the Volta-based Tesla V100 GPU in 2017 revolutionized mixed-precision training, supporting FP16 operations with FP32 accumulation to maintain accuracy while boosting throughput. These cores delivered up to 125 TFLOPS of Tensor performance, enabling substantial speedups in matrix multiply-accumulate operations central to . For instance, training the ResNet-50 model on , which required weeks on CPU clusters, was reduced to hours on V100 clusters, highlighting the impact on large-scale model development. Data center deployments like the DGX-1 , announced in 2016 and updated with V100 GPUs in 2017, bundled multiple Tesla accelerators interconnected via for , providing turnkey platforms that accelerated tasks by up to 3x compared to prior GPU systems. This integration of hardware, software, and optimized libraries positioned Tesla GPUs as a cornerstone for advancing capabilities in environments.

Products and Specifications

Key Product Generations

The Nvidia Tesla product line began with the G80 architecture in 2007, followed by the series in 2008, introducing GPU computing accelerators optimized for tasks. The Tesla C870 featured a peak single-precision performance of 0.35 TFLOPS and 1.5 GB of GDDR3 memory, marking an early step in dedicated compute GPUs without graphics outputs. The subsequent C1060, released in 2008, advanced this with 0.93 TFLOPS peak performance and 4 GB of GDDR3 memory, enabling larger-scale in server environments. The Fermi architecture generation, launched in 2010-2011, emphasized improved double-precision capabilities for scientific simulations. The Tesla C2050 delivered 0.52 TFLOPS in FP64 performance alongside 3 GB of GDDR5 memory, while the C2075 variant doubled the memory to 6 GB with 0.52 TFLOPS FP64, supporting more complex datasets in HPC workloads. Kepler-based products from 2012-2014 further enhanced energy efficiency and double-precision throughput. The K10 provided 0.19 TFLOPS FP64 total in a dual-GPU configuration, suitable for entry-level acceleration. The K20 offered 1.17 TFLOPS FP64 with 5 GB GDDR5, the K40 scaled to 7 TFLOPS FP64 and 12 GB GDDR5 for demanding applications, and the dual-GPU K80 achieved 2.91 TFLOPS FP64 total (1.46 TFLOPS per GPU). In the Maxwell era of 2015, Tesla products integrated higher for and simulation tasks, though with reduced emphasis on double-precision. The M40 delivered 7 TFLOPS FP32 performance with 12 GB GDDR5, while the dual-GPU provided 0.15 TFLOPS FP64 total and 16 GB GDDR5, targeted at and multi-user environments. The Pascal generation in introduced high-bandwidth memory. The P100 reached 5.3 TFLOPS FP64 peak with 16 GB HBM2 memory, enabling faster data access for HPC and workloads. The Volta generation debuted in 2017 with the V100, offering 7.8 TFLOPS FP64 performance and up to 32 GB HBM2 memory in both PCIe and SXM form factors, optimizing for and large-scale computing. The Turing generation in focused on inference efficiency with the T4, providing 8.1 TFLOPS FP32, 0.13 TFLOPS FP64, 16 GB GDDR6 memory, and 320 Tensor Cores at 70 W TDP for low-power AI deployments. Tesla products were available in various form factors, including standard PCIe cards for rack servers and modules designed for dense computing environments.

Performance and Compatibility Details

The Nvidia Tesla series demonstrates significant evolution in peak performance, particularly in single-precision floating-point (FP32) operations, scaling from 0.35 TFLOPS in early models based on the G80 architecture, such as the Tesla C870, to 15.7 TFLOPS in the V100 GPU. This progression reflects architectural advancements across generations: Fermi-based models like the M2050 achieved 1.03 TFLOPS FP32, Kepler's K20 reached 3.52 TFLOPS, Pascal's P100 delivered 10.6 TFLOPS, and Volta's V100 pushed to 15.7 TFLOPS, enabling substantial gains in compute-intensive workloads. Power efficiency has also improved markedly, with thermal design power (TDP) evolving alongside performance metrics. For instance, the Kepler K20X offered a 3x increase in double-precision (FP64) performance over Fermi predecessors like the M2070 at similar power envelopes, achieving up to 1.31 TFLOPS FP64 while maintaining a 225 W TDP. The Pascal P100, with a 300 W TDP, provided 5.3 TFLOPS FP64, yielding approximately 0.018 TFLOPS/W—a notable enhancement over earlier generations through optimized core designs and memory hierarchies. Later models like the V100 sustained high efficiency at 300 W, with modes allowing trade-offs between peak performance and energy savings. Benchmark results highlight real-world capabilities, particularly in tests like LINPACK. In the , comprising 18,688 K20X GPUs, the system achieved 17.59 PetaFLOPS sustained performance on LINPACK, representing about 65% of its 27 PetaFLOPS peak, underscoring the K20's efficiency in large-scale clusters. For tensor operations, the V100's 640 Tensor Cores delivered up to 125 TFLOPS in workloads, a breakthrough that accelerated matrix-heavy computations beyond traditional FP32 limits. Compatibility across the Tesla lineup is facilitated by Nvidia's toolkit, which debuted with version 1.0 in 2007 alongside the initial G80-based products, enabling on and operating systems. Support extended through 10.0 by 2018, encompassing architectures from to , with drivers ensuring seamless integration for compute tasks on enterprise servers. A key limitation of Tesla GPUs is the absence of display connectivity, as they are optimized solely for compute acceleration without video output ports, necessitating a separate graphics card for any visualization needs in server environments.

Legacy and Impact

Technological Influence

The Nvidia Tesla GPUs, particularly from the Kepler and Pascal generations onward, laid foundational technologies that directly influenced the evolution of data center GPUs, culminating in the Ampere and Hopper architectures post-2018. The Tesla V100, based on the Volta architecture, featured error-correcting code (ECC) memory—a capability present since the Fermi architecture—for enhanced reliability in high-performance computing environments, a feature retained and refined in the A100 (Ampere) with 40GB or 80GB HBM2e memory supporting ECC, and further advanced in the H100 (Hopper) with up to 141GB HBM3e memory also featuring ECC to mitigate data corruption in large-scale AI training. Similarly, NVLink interconnect technology, first deployed in the Pascal-based Tesla P100 for high-bandwidth GPU-to-GPU communication at up to 300 GB/s bidirectional, was inherited and upgraded in subsequent architectures: NVLink 3.0 in the A100 provided 600 GB/s per GPU pair, while NVLink 4.0 in the H100 scaled to 900 GB/s, enabling seamless multi-GPU scaling that originated from Tesla's emphasis on clustered computing. These inheritances transformed Tesla's compute-focused designs into the backbone of modern data center hardware, shifting Nvidia's ecosystem toward unified, high-density AI and HPC deployments. The Tesla series also standardized GPU computing through CUDA, Nvidia's parallel computing platform introduced in 2006 and matured via Tesla hardware, establishing it as the de facto industry norm for accelerated workloads. By providing a robust, vendor-optimized API for general-purpose GPU (GPGPU) programming, CUDA's widespread adoption in Tesla GPUs compelled competitors to develop alternatives, such as AMD's ROCm platform launched in 2016 to enable open-source GPU acceleration on Radeon Instinct hardware, and Intel's oneAPI unified programming model introduced in 2019 to support heterogeneous computing across CPUs, GPUs, and FPGAs, both explicitly positioned as responses to CUDA's dominance in scientific and AI applications. This standardization elevated GPU computing from niche experimentation to essential infrastructure, with CUDA's ecosystem of libraries like cuDNN and cuBLAS becoming benchmarks that influenced cross-vendor portability efforts. Tesla innovations profoundly impacted supercomputing by enabling GPU acceleration in TOP500 systems, transitioning the field from CPU-centric designs to hybrid architectures. Early Tesla GPUs like the Fermi-based C2050 powered breakthroughs such as China's Nebulae supercomputer in 2010, which ranked second on the list with Nvidia GPUs contributing to its 1.27 petaflops performance. Throughout the , this shifted dominance, with GPU-accelerated systems rising from a handful in 2010 to contributing 56% of new flops added to the by June 2018, driven by Tesla V100 deployments in machines like , which topped the list in 2018 with 4,608 V100 GPUs delivering 122.3 petaflops. By the end of the decade, accelerators powered over 30% of TOP500 entries, underscoring Tesla's role in democratizing exascale potential. Key intellectual property from Tesla extended to specialized hardware features that prefigured contemporary AI accelerators. The Tensor Cores, first introduced in the Tesla V100 with 640 units delivering up to 125 teraflops in FP16 for matrix operations central to , served as the precursor to optimized AI compute in later architectures like 's third-generation Tensor Cores and Hopper's fourth-generation, which support FP8 precision for even faster inference. Additionally, scalability concepts in Tesla GPUs, such as the Multi-Process Service () in enabling concurrent process sharing on a single GPU without full isolation, evolved into the Multi-Instance GPU () feature in Ampere and beyond, allowing secure partitioning into up to seven isolated instances with dedicated memory and compute—directly addressing Tesla-era challenges in multi-tenant efficiency. This legacy persists into the , as evidenced by cloud services like AWS EC2 P3 instances, launched in with up to eight Tesla V100 GPUs per node, continuing to support AI workloads in production environments as of 2025.

Adoption and Case Studies

The Nvidia Tesla GPUs have been integral to several landmark supercomputing deployments, demonstrating their scalability in high-performance computing environments. The Tianhe-1A supercomputer, operational since 2010 at the National Supercomputing Center in Tianjin, China, utilized 7,168 Nvidia Fermi-based Tesla GPUs alongside Intel Nehalem CPUs to achieve a peak performance of 4.7 petaFLOPS and a Linpack benchmark score of 2.507 petaFLOPS, making it the world's fastest system at the time and highlighting early GPU acceleration for heterogeneous computing. Similarly, the Summit supercomputer, deployed in 2018 at Oak Ridge National Laboratory in the United States, incorporated 27,648 Nvidia Tesla V100 GPUs across 4,608 nodes, delivering a peak performance of 200 petaFLOPS and enabling breakthroughs in simulations for climate modeling, materials science, and genomics. In the oil and gas sector, Tesla GPUs facilitated advanced simulations critical for resource and . In 2017, Stone Ridge Technology, in collaboration with and , achieved a record-breaking billion-cell simulation using 120 Nvidia Tesla P100 GPUs on 30 Minsky nodes, completing the computation in 92 minutes—outperforming a prior CPU-based approach by that required over six hours on thousands of processors and demonstrating up to 10x efficiency gains in for seismic and modeling. Tesla GPUs have also driven significant research impacts in artificial intelligence and healthcare. For AI advancements, distributed clusters of Tesla V100 GPUs supported the training of large-scale neural networks, contributing to efficiency improvements in deep learning workloads during the late 2010s. In healthcare, particularly for COVID-19 research in 2020, a team led by the University of California, San Diego, in collaboration with Argonne National Laboratory and NVIDIA, leveraged Tesla V100 GPUs within the Nvidia Clara Discovery platform on the Summit supercomputer to accelerate molecular dynamics simulations of the SARS-CoV-2 spike protein, winning a special Gordon Bell Prize for providing atomic-level insights into viral structure and interactions that aid drug and vaccine design. Adoption of Tesla GPUs peaked during 2015-2018, coinciding with the rise of deep learning and HPC demands, though exact shipment figures for data center units remain proprietary; Nvidia's overall data center revenue grew rapidly in this period, reflecting widespread integration into enterprise and research infrastructures. By 2025, refurbished V100 units continue to see use in legacy high-performance computing setups and cost-sensitive edge AI applications, such as on-premises inference for smaller-scale machine learning tasks in industrial IoT, where their tensor core capabilities provide value despite newer alternatives.

References

  1. [1]
    [PDF] NVIDIA Tesla
    NVIDIA Tesla is a high-performance computing solution using GPU computing, with products like C870, D870, and S870, for workstations and servers.
  2. [2]
    [PDF] nvidia® tesla® gpu computing
    TESLA GPU COMPUTING SOLUTIONS. NVIDIA Tesla products are designed for high-performance computing, and offers exclusive computing features. Superior ...
  3. [3]
    History of Nvidia: Company timeline and facts - TheStreet
    Jul 13, 2025 · 2007: Nvidia launches Tesla products (no relation to Elon Musk's Tesla TSLA) for scientific and engineering computing use. A Tesla could perform ...
  4. [4]
    [PDF] TESLA™ C2050 / C2070 GPU ComPUTinG ProCESSor - NVIDIA
    The Tesla C2050 and. C2070 GPUs are designed to redefine high performance computing and make supercomputing available to everyone. Compared to the latest quad- ...
  5. [5]
    [PDF] NVIDIA® Tesla® GPU Accelerators Datasheet - | HPC @ LLNL
    NVIDIA Tesla GPUs are for HPC, built on Kepler architecture, powered by CUDA, and ideal for big data applications. The K40 has 12GB memory and outperforms CPUs ...
  6. [6]
    NVIDIA Tesla V100
    The NVIDIA Tesla V100 is the first Tensor Core GPU, a data center GPU for AI, HPC, and data science, with 640 Tensor Cores and 100 CPU performance.Welcome To The Era Of Ai · Ai Training · Ai Inference
  7. [7]
    [PDF] NVIDIA TESLA GPUs FOR VIRTUALIZATION
    NVIDIA Tesla GPUs enable virtual desktops/workstations, shared across multiple virtual machines, with up to 24 virtual desktops per GPU.
  8. [8]
    [PDF] NVIDIA TESLA V100 GPU ARCHITECTURE
    The V100 architecture includes Volta Streaming Multiprocessor, Tensor Cores, enhanced L1 cache, and HBM2 memory. It is designed for AI and HPC.
  9. [9]
    NVIDIA ® Tesla ® K80
    The Tesla K80 boosts throughput 5-10x, has 4992 CUDA cores, up to 2.91 teraflops double-precision, 24 GB GDDR5 memory, and 480 GB/s bandwidth.<|control11|><|separator|>
  10. [10]
    High Performance Supercomputing | NVIDIA Data Center GPUs
    Plus, NVIDIA GPUs deliver the highest performance and user density for virtual desktops, applications, and workstations.Data Center Gpus For Servers · Nvidia Gpu-Accelerated... · High Performance Computing
  11. [11]
    Nvidia Confirms It Dropped Tesla Branding Years Ago
    May 20, 2020 · In fact, according to the representative, Nvidia ditched the Tesla name back in 2018 with the Nvidia T4. However, Nvidia originally dubbed the ...
  12. [12]
    NVIDIA Virtual GPU Software Lifecycle on Supported GPUs
    A. End-of-Software-Support Dates for GPUs Supported by NVIDIA vGPU Software ; Tesla V100 SXM2 32 GB, 2021-06, 2024-06 ; Tesla V100S PCIe 16 GB, 2022-01, 2025-01 ...
  13. [13]
    NVIDIA Discontinues the Tesla Brand to Avoid Confusion with Tesla ...
    May 20, 2020 · The company has decided to discontinue "Tesla" as the top-level brand for its HPC, AI, and scalar compute accelerator product line.
  14. [14]
    [PDF] nvidia tesla:aunified graphics and computing architecture
    NVIDIA's Tesla architecture, introduced in November 2006 in the GeForce 8800 GPU, unifies the vertex and pixel processors and extends them, enabling high- ...
  15. [15]
    NVIDIA Tesla: GPU computing gets its own brand - Beyond3D
    Jun 20, 2007 · A Brief History of CUDA. When NVIDIA's G80 launched in November 2006, there was a brief mention of a new toolkit that would greatly simplify ...
  16. [16]
    [PDF] FermiTM - NVIDIA
    Sep 30, 2009 · Introduced in November 2006, the G80 based GeForce 8800 brought several key innovations to GPU Computing: • G80 was the first GPU to support C, ...
  17. [17]
    NVIDIA Unveils Tesla GPU Computing Processor | TechPowerUp
    Jun 20, 2007 · The Tesla S870, D870 and C870 carry an MSRP of $12,000, $1,499 and $7,500, respectively. and: The Tesla S870 consumes up to 800-watts of ...
  18. [18]
    Nvidia GPGPU line sparks into life with Tesla - The Register
    Jun 21, 2007 · The Tesla kit leverages the Cuda GPGPU software Nvidia introduced back in November 2006, it's first foray against what was then ATI's Stream ...
  19. [19]
    First-Ever Showing of NVIDIA Tesla GPU Server at SuperComputing ...
    Nov 8, 2007 · At SuperComputing 2007, NVIDIA will be demonstrating its new Tesla™ family of GPU high-performance computing (HPC) solutions, including the ...
  20. [20]
    [PDF] NVIDIA® Tesla
    May 24, 2007 · in the VMD tool reaches 705 gigaflops of realized performance. This remarkable performance allows any bioscience researcher to have the ...<|control11|><|separator|>
  21. [21]
    [PDF] TESLA™ C2050 / C2070 GPU ComPUTinG ProCESSor - NVIDIA
    The NVIDIA Tesla™ c2050 and c2070 computing Processors fuel the transition to parallel computing and bring the performance of a small cluster to the desktop.
  22. [22]
    NVIDIA Unleashes Fermi GPU for HPC - HPCwire
    Nov 15, 2009 · The x2050 models come with 3 GB per GPU (2.625 GB per GPU with ECC enabled), while the x2070 models double that to 6 GB per GPU (5.25 GB per GPU ...
  23. [23]
    NVIDIA Unveils World's Fastest, Most Efficient Accelerators, Powers ...
    Nov 11, 2012 · The new family also includes the Tesla K20 accelerator, which provides 3.52 teraflops of single-precision and 1.17 teraflops of double-precision ...
  24. [24]
    [PDF] NVIDIA® TESLA® KEPLER GPU COMPUTING ACCELERATORS
    K20 delivers 3x the double precision performance compared to the previous generation Fermi-based Tesla M2090, in the same power envelope. Tesla K20 features ...Missing: K80 | Show results with:K80
  25. [25]
    [PDF] Tesla K80 GPU Accelerators - NVIDIA
    CPU system: single E5-2697v2 @ 2.70 GHz, Centos 6.2, 64 GB System memory. 1 Tesla K80 specifications are shown as aggregate of two GPUs.Missing: K20 | Show results with:K20
  26. [26]
    Introducing the NVIDIA Tesla K80 GPU Accelerator (Kepler GK210)
    Nov 17, 2014 · 8.74 TFLOPS single precision, 2.91 TFLOPS double precision with GPU Boost; 300W TDP. To achieve this performance, Tesla K80 is really two GPUs ...Missing: K20 | Show results with:K20
  27. [27]
    [PDF] nVIDIa® Tesla® M40 - GPU aCCeleRaTOR
    The Tesla M40 accelerator provides a powerful foundation for customers to leverage best-in-class software and solutions for deep learning. NVIDIA cuDNN, DIGITS™ ...
  28. [28]
    Nvidia Brings Maxwell GPUs To Tesla Coprocessors
    Nov 10, 2015 · At 28 gigaflops per watt, the Tesla M40 is almost as power efficient as the Tesla M4 and the Tesla K80, and is around 40 percent more power ...Missing: M60 | Show results with:M60
  29. [29]
    In-Depth Comparison of NVIDIA Tesla "Maxwell" GPU Accelerators
    Mar 4, 2016 · Energy-efficiency – Maxwell GPUs deliver nearly twice the power-efficiency of Kepler GPUs. SMM architecture – the Maxwell Multiprocessor ...
  30. [30]
    [PDF] NVIDIa® Tesla® P100 - GPU aCCeleRaTOR
    P100. 3X memory boost. M40. PASCAL ARCHITECTURE. More than 21 TeraFLOPS of FP16, 10. TeraFLOPS of FP32, and 5 TeraFLOPS of FP64 performance powers new.
  31. [31]
    NVIDIA Delivers Massive Performance Leap for Deep Learning ...
    Apr 5, 2016 · New AI algorithms for peak performance -- New half-precision instructions deliver more than 21 teraflops of peak performance for deep learning.
  32. [32]
    [PDF] Pascal Architecture Whitepaper | NVIDIA Tesla P100
    Tesla P100 with its 3584 processing cores delivers over 21 TFLOPS of FP16 processing power for Deep Learning applications. Interconnecting eight Tesla P100 ...
  33. [33]
    [PDF] NVIDIA TESLA V100 GPU ACCELERATOR
    TENSOR CORE​​ Equipped with 640 Tensor Cores, Tesla V100 delivers 120 TeraFLOPS of deep learning performance. That's 12X Tensor FLOPS for DL Training, and 6X ...<|separator|>
  34. [34]
    Nvidia Tesla V100: First Volta GPU is one of the largest silicon chips ...
    May 11, 2017 · In addition, V100 also features 672 tensor cores (TCs), a new type of core explicitly designed for machine learning operations.
  35. [35]
    Nvidia Unifies AI Compute With “Ampere” GPU - The Next Platform
    May 14, 2020 · To my knowledge, A100 is the successor to T4 and V100, as the story was themed around. No more different GPUs for inference and training. But, ...Missing: brand discontinuation
  36. [36]
    Nvidia has killed two of its iconic brands - here's why | TechRadar
    Oct 15, 2020 · Nvidia plans to retire its Quadro and Tesla brands and cover both professional graphics and compute markets using just one brand, its own name.
  37. [37]
    [PDF] TESLA V100 PCIe GPU ACCELERATOR - NVIDIA
    Sep 19, 2017 · It uses a passive heat sink for cooling, which requires system air flow to properly operate the card within its thermal limits.Missing: core principles
  38. [38]
    [PDF] TESLA 1U GPU COMPUTING SYSTEM - NVIDIA
    Mar 22, 2010 · The Tesla S2050 use a pair of rails for mounting to a 4-post, EIA rack. The rails can expand to fit a distance from 730 mm (28.74 inches) to 922 ...
  39. [39]
    NVIDIA Tesla C1060 Specs - GPU Database - TechPowerUp
    NVIDIA has paired 1,024 MB GDDR3 memory with the Tesla C1060, which are connected using a 512-bit memory interface. The GPU is operating at a frequency of ...
  40. [40]
    [PDF] NVIDIA DGX-1 with Tesla V100 System Architecture White paper
    In addition to the 512 GB of system memory, the eight Tesla V100 GPUs have a total of 128 GB HBM2 memory with net GPU memory bandwidth of 8 × 900 GB/s = 7.2 TB/ ...
  41. [41]
    [PDF] NVIDIA's Fermi: The First Complete GPU Computing Architecture
    Fermi's ECC protection for DRAM is unique among GPUs; so is its implementation. Instead of each 64‐bit memory channel carrying eight extra bits for ECC ...
  42. [42]
    [PDF] TESLA K20 GPU ACTIVE ACCELERATOR - NVIDIA
    Oct 9, 2012 · ▻ Number of processor cores: 2496. ▻ Processor core clock: 706 MHz ... NVIDIA, the NVIDIA logo, CUDA, and Tesla are trademarks and/or registered ...
  43. [43]
    1. Introduction — Floating Point and IEEE 754 13.0 documentation
    Current generations of the NVIDIA architecture such as Tesla Kxx, GTX 8xx, and GTX 9xx, support both single and double precision with IEEE 754 precision and ...
  44. [44]
    [PDF] Molecular Dynamics (MD) on GPUs - NVIDIA
    Double precision is important. Uses cuBLAS, cuFFT, CUDA. Uses cuBLAS ... • K80 GPU is our fastest and lowest power high performance GPU yet. Try GPU ...
  45. [45]
    [PDF] nvidia® tesla® gpu computing
    The latest generation CUDA architecture, codenamed “Fermi”, is the most advanced GPU computing architecture ever built. With over three billion transistors, ...
  46. [46]
    MPI Solutions for GPUs - NVIDIA Developer
    MPI is fully compatible with CUDA, CUDA Fortran, and OpenACC, all of which are designed for parallel computing on a single computer or node.
  47. [47]
    June 2013 | TOP500
    Jun 28, 2013 · Two heterogeneous systems, based on NVIDIA's Kepler K20 GPU accelerators, claim the top two positions and break through the three-billion ...Missing: K40 | Show results with:K40
  48. [48]
    November 2013 | TOP500
    Each computational node within TSUBAME-KFC consists of two Intel Ivy Bridge processors and four NVIDIA Kepler GPUs. In fact, all systems in the top ten of the ...Missing: K40 | Show results with:K40
  49. [49]
    NVIDIA Powers Titan, World's Fastest Supercomputer For Open ...
    Oct 28, 2012 · Titan's peak performance is more than 20 petaflops -- or 20 ... 18,688 NVIDIA® Tesla® K20 GPU accelerators. These are based on the ...
  50. [50]
    Accelerate Machine Learning with the cuDNN Deep Neural Network ...
    Sep 7, 2014 · NVIDIA is introducing a library of primitives for deep neural networks called cuDNN. The cuDNN library makes it easy to obtain state-of-the-art performance ...
  51. [51]
    [1410.0759] cuDNN: Efficient Primitives for Deep Learning - arXiv
    Oct 3, 2014 · cuDNN is a library of efficient deep learning primitives, similar to BLAS, that improves performance and memory usage in frameworks like Caffe.
  52. [52]
    Train With Mixed Precision - NVIDIA Docs
    Feb 1, 2023 · The theoretical peak performance of the Tensor Cores on the V100 is approximately 120 TFLOPS. This is about an order of magnitude (10x) faster ...
  53. [53]
    NVIDIA Launches World's First Deep Learning Supercomputer
    Apr 5, 2016 · General availability for the NVIDIA DGX-1 deep learning system in the United States is in June, and in other regions beginning in the third ...
  54. [54]
    [PDF] Tesla C1060 Computing Processor Board - NVIDIA
    Sep 22, 2008 · The Tesla C1060 board supports the following internal connectors and headers. ❑ 8-pin PCI Express power connector (can be used with a 6-pin ...
  55. [55]
    [PDF] nvidia® tesla™ c2075 companion processor
    The Tesla C2075 offloads CPU computations, accelerating calculations exponentially, with up to 515 Gflops double-precision performance, and 448 CUDA cores.Missing: C2050 | Show results with:C2050
  56. [56]
    [PDF] Tuning CUDA Applications for Kepler - NVIDIA Docs
    NVIDIA's Tesla K10 GPU Accelerator is a dual-GK104 board; the Tesla K80 GPU. Accelerator is a dual-GK210 board. As with other dual-GPU NVIDIA boards, the two.
  57. [57]
    https://www.techpowerup.com/gpu-specs/tesla-k20c.c564
  58. [58]
    NVIDIA Tesla P100 Supercharges HPC Applications by More Than ...
    Jun 19, 2016 · 16GB of CoWoS HBM2 stacked memory, delivering 720GB/sec of memory bandwidth; 12GB of CoWoS HBM2 stacked memory, delivering 540GB/sec of memory ...Missing: M40 M60
  59. [59]
    [PDF] NVIDIA TESLA V100 GPU ACCELERATOR
    With a combination of improved raw bandwidth of 900GB/s and higher DrAM utilization efficiency at 95%, Tesla V100 delivers 1.5X higher memory bandwidth over ...Missing: 288 | Show results with:288
  60. [60]
    NVIDIA G80 GPU Specs - TechPowerUp
    NVIDIA's G80 GPU uses the Tesla architecture and is made using a 90 nm production process at TSMC. With a die size of 484 mm² and a transistor count of 681 ...
  61. [61]
    NVIDIA Tesla M2050 Specs - GPU Database - TechPowerUp
    FP32 (float): 1,030.4 GFLOPS. FP64 (double): 515.2 GFLOPS (1:2). Board Design. Slot Width: Dual-slot. Length: 248 mm 9.8 inches. TDP: 225 W. Suggested PSU: 550 ...Missing: TFLOPS | Show results with:TFLOPS
  62. [62]
    Nvidia unveils first Pascal graphics card, the monstrous Tesla P100
    Apr 6, 2016 · Nvidia says the P100 reaches 21.2 teraflops of half-precision (FP16) floating point performance, 10.6 teraflops of single precision (FP32), and ...
  63. [63]
    [PDF] Inside Kepler - Tesla K20 Family: 3x Faster Than Fermi - NVIDIA
    Tesla M2090. Tesla K20X. TFLO. PS .18 TFLOPS .43 TFLOPS. 1.22 TFLOPS. Double Precision FLOPS (DGEMM). Tesla K20X. Page 4. Tesla K20 over Fermi Acceleration.
  64. [64]
    NVIDIA Pascal GP100 Architecture Deep-Dive | GamersNexus
    May 6, 2016 · It's rated for a 300W TDP and offers a staggering 5.3 TFLOPS of FP64 (double-precision) COMPUTE performance and 10.6 TFLOPS of FP32. FP16 is ...<|separator|>
  65. [65]
    [PDF] NVIDIA TESLA V100 GPU ARCHITECTURE
    Prior NVIDIA GPU SIMT Models ... In 2016, NVIDIA launched the first generation DGX-1 featuring eight NVIDIA Tesla P100 GPUs.
  66. [66]
    NVIDIA Tensor Core Programmability, Performance & Precision
    The NVIDIA Tesla V100 accelerator, featuring the Volta microarchitecture, provides 640 Tensor Cores with a theoretical peak performance of 125 Tflops/s in mixed ...
  67. [67]
    CUDA - Wikipedia
    CUDA works with all Nvidia GPUs from the G8x series onwards, including GeForce, Quadro and the Tesla line. CUDA is compatible with most standard operating ...Nvidia CUDA Compiler · Tesla (microarchitecture) · ROCm · Shader
  68. [68]
    [PDF] NVIDIA CUDA and Drivers Support
    This driver branch supports CUDA. 10.2, CUDA. 11.0 and CUDA. 11.1 (through. CUDA forward compatible upgrade). CUDA 10.2. This driver branch supports CUDA. 11.0 ...
  69. [69]
    Release Notes :: CUDA Toolkit Documentation - NVIDIA Docs
    Aug 1, 2018 · CUDA 9.2 (9.2.148) Update 1 is a bug fix update to the CUDA 9.2 Toolkit. The update includes fixes to issues in the CUDA Libraries (see ...Missing: history | Show results with:history
  70. [70]
    Tesla V100 + Nvidia 455.32.00: UseDisplayDevice "None" is not ...
    NVidia Tesla's are designed as GPU compute cards and Graphics accelerators. They literally don't have any VGA/DisplayPort/HDMI ports on the back of the card.Missing: limitations | Show results with:limitations
  71. [71]
    NVIDIA GPUs power world's fastest supercomputer - Phys.org
    Oct 29, 2010 · By using NVIDIA's GPUs in a heterogeneous computing environment, Tianhe-1A consumes only 4.04 megawatts, making it 3 times more power efficient.
  72. [72]
    Summit - Oak Ridge Leadership Computing Facility
    System Specifications ; GPUs: 27,648 NVIDIA Volta V100s (6/node) ; Nodes: 4,608 ; Node Performance: 42TF ; Memory/node: 512GB DDR4 + 96GB HBM2 ; NV Memory/node: ...
  73. [73]
    Oil And Gas Upstart Has No Reserves About GPUs - The Next Platform
    Jul 24, 2017 · But, in the end we did it on 30 Minsky nodes, with a total of 120 Nvidia P100 GPU accelerators, and we did the whole run in about 90 minutes.
  74. [74]
    COVID-19 Spurs Scientific Revolution in Drug Discovery with AI
    Accelerated by NVIDIA Clara Discovery, one team's research wins a special Gordon Bell Prize for COVID-19. November 19, 2020 by Geetika Gupta.
  75. [75]
    Biomedical Text Link Prediction for Drug Discovery: A Case Study ...
    Training was done on a server with configuration of 1 NVIDIA TESLA v100 ... COVID-19: Discovery, diagnostics and drug development. J. Hepatol. 2020 doi ...
  76. [76]
    Should You Still Buy NVIDIA Tesla V100 in 2025? Pros and Cons
    Buying refurbished units comes with risks: limited warranty, reduced reliability, and a lack of long-term support. Software & Driver Support in 2025. Even ...Missing: studies | Show results with:studies