The NVIDIA Tesla™ C2050 and C2070 by PNY fuel the transition to parallel computing and bring the performance of a small cluster to the desktop. Based on the next-generation CUDA architecture, the 20-series family of Tesla GPUs support many “must have” features for technical and enterprise computing including C++ support, ECC memory for uncompromised accuracy and scalability, and a 7X increase in double precision performance compared Tesla 10-series GPUs. Compared to the latest quad-core CPUs, Tesla C2050 and C2070 by PNY deliver equivalent supercomputing performance at 1/10th the cost and 1/20th the power consumption EATURES GPUs powered by the Fermi-generation of the CUDA architecture Delivers cluster performance at 1/10th the cost and 1/20th the power of CPU-only systems based on the latest quad core CPUs. 448 CUDA Cores Delivers up to 515 Gigaflops of double-precision peak performance in each GPU, enabling a single workstation to deliver a Teraflop or more of performance. Single precision peak performance is over a Teraflop per GPU. ECC Memory Meets a critical requirement for computing accuracy and reliability for workstations. Offers protection of data in memory to enhance data integrity and reliability for applications. Register files, L1/L2 caches, shared memory, and DRAM all are ECC protected. Desktop Cluster Performance Solves large-scale problems faster than a small server cluster on a single workstation with multiple GPUs. 6GB of GDDR5 memory per GPU Maximizes performance and reduces data transfers by keeping larger data sets in local memory that is attached directly to the GPU. NVIDIA Parallel DataCache™ Accelerates algorithms such as physics solvers, ray-tracing, and sparse matrix multiplication where data addresses are not known beforehand. This includes a configurable L1 cache per Streaming Multiprocessor block and a unified L2 cache for all of the processor cores. NVIDIA GigaThread™ Engine Maximizes the throughput by faster context switching that is 10X faster than previous architecture, concurrent kernel execution, and improved thread block scheduling. Asynchronous Transfer Turbocharges system performance by transferring data over the PCIe bus while the computing cores are crunching other data. Even applications with heavy data-transfer requirements, such as seismic processing, can maximize the computing efficiency by transferring data to local memory before it is needed. CUDA programming environment with broad support of programming languages and APIs Choose C, C++, OpenCL, DirectCompute, or Fortran to express application parallelism and take advantage of the “Fermi” GPU’s innovative architecture. NVIDIA Parallel Nsight™ tool is available for Microsoft Visual Studio developers. High Speed , PCIe Gen 2.0 Data Transfer Maximizes bandwidth between the host system and the Tesla processors. Enables Tesla systems to work with virtually any PCIe-compliant host system with an open PCIe x16 slot.