{"id":1640,"date":"2025-08-29T11:20:38","date_gmt":"2025-08-29T10:20:38","guid":{"rendered":"https:\/\/shyuxinpc.com\/?post_type=product&p=1640"},"modified":"2025-09-12T07:28:14","modified_gmt":"2025-09-12T06:28:14","slug":"nvidia-a800-40gb-active","status":"publish","type":"product","link":"https:\/\/shyuxinpc.com\/en_gb\/product\/nvidia-a800-40gb-active\/","title":{"rendered":"NVIDIA A800 40GB Active"},"content":{"rendered":"

Powerful Performance and Versatility
\nfor Data Science and HPC Workloads<\/h1>\n

The explosion of new and emerging analytics, artificial intelligence (AI) and high-performance computing (HPC) applications are fueling the next industrial revolution and transforming how creative and knowledge workers conquer their greatest challenges. Solving these challenges requires powerful software and hardware platforms that can accelerate the development and deployment of next-generation applications and simplify the process of deploying at scale.<\/p>\n

The NVIDIA A800 40GB Active GPU, powered by the NVIDIA Ampere architecture, delivers unprecedented compute acceleration for workstations, delivering powerful performance to accelerate the development and deployment of next-generation data science, data analytics, AI and HPC applications. By using common frameworks to develop once and deploy anywhere, IT and creative professionals can leverage high-performance workstation platforms powered by A800 40GB Active to derive insights from large datasets, build, iterate, and refine AI-augmented applications and models, tackle the most demanding computational problems, and simplify deployment at scale. Supercomputing power on workstation platforms with A800 40GB Active, combined with NVIDIA\u2019s GPU-optimized data science, AI and HPC software platforms, preserves scarce data center resources by using common frameworks to build once and deploy anywhere at scale.<\/p>\n

Third-Generation Tensor Cores<\/b><\/h4>\n
Performance and Versatility for HPC and AI<\/b><\/h5>\n

First introduced in the NVIDIA Volta\u2122<\/sup>\u00a0architecture, NVIDIA Tensor Core technology has brought dramatic speedups to AI training and inference operations, bringing down training times from weeks to hours and providing massive acceleration to inference.<\/p>\n

The NVIDIA Ampere architecture builds upon these innovations by providing up to 20X higher FLOPS for AI. It does so by improving the performance of existing precisions and bringing new precisions\u2014TF32, INT8, and FP64\u2014that accelerate and simplify AI adoption and extend the power of NVIDIA Tensor Cores to HPC.<\/p>\n

Multi-Instance GPU (MIG)<\/b><\/h4>\n
Securely, Isolated Multi-Tenancy<\/b><\/h5>\n

Each AI and HPC application can benefit from acceleration, but not every application needs the performance of a full A800 40GB Active GPU. Multi-Instance GPU (MIG) maximizes the utilization of GPU-accelerated infrastructure by allowing an A800 40GB Active GPU to be partitioned into as many as seven independent instances, fully isolated at the hardware level. This provides multiple users access to GPU acceleration with their own high-bandwidth memory, cache, and compute cores. Now, developers can access breakthrough acceleration for all their applications, big and small, and get guaranteed quality of service. And IT administrators can offer right-sized GPU acceleration for optimal utilization and expand access to every user and application.<\/p>","protected":false},"excerpt":{"rendered":"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Architecture<\/td>\nNVIDIA Ampere<\/td>\n<\/tr>\n
CUDA Cores<\/td>\n6912<\/td>\n<\/tr>\n
Tensor Cores<\/td>\n432<\/td>\n<\/tr>\n
GPU Memory<\/td>\n40 GB HBM2<\/td>\n<\/tr>\n
Peak Double Precision (FP64) Performance<\/td>\n9.7 TFLOPS<\/td>\n<\/tr>\n
FP64 Tensor Core Performance<\/td>\n19.5 TFLOPS<\/td>\n<\/tr>\n
Peak Single Precision (FP32) Performance<\/td>\n19.5 TFLOPS<\/td>\n<\/tr>\n
Tensor Float 32 (TF32) Tensor Core Performance<\/td>\n311.8 TFLOPS<\/td>\n<\/tr>\n
Peak Half Precision (FP16) Performance<\/td>\n78.0 TFLOPS<\/td>\n<\/tr>\n
BFLOAT16 Tensor Core Performance<\/td>\n623.8 TFLOPS<\/td>\n<\/tr>\n
Peak Integer Operation (INT8) Performance<\/td>\n1247.4 TOPS<\/td>\n<\/tr>\n
Peak Tensor Operation Performance1<\/sup><\/td>\n623.8 TFLOPS<\/td>\n<\/tr>\n
Memory Interface<\/td>\n5120-bit<\/td>\n<\/tr>\n
Memory Bandwidth<\/td>\n1555.2 GB\/s<\/td>\n<\/tr>\n
Max Power Consumption<\/td>\n240W<\/td>\n<\/tr>\n
Thermal Solution<\/td>\nBlower Active Fan<\/td>\n<\/tr>\n
Multi-Instance GPU<\/td>\nUp to 7 MIGs @5GB<\/td>\n<\/tr>\n
Graphics Bus<\/td>\nPCIe 4.0 x16<\/td>\n<\/tr>\n
Display Connectors<\/td>\nNo Display Output Supported<\/td>\n<\/tr>\n
Form Factor<\/td>\n4.4\u201d H x 10.5\u201d L Dual Slot<\/td>\n<\/tr>\n
Product Weight<\/td>\n1181.9g<\/td>\n<\/tr>\n
vGPU Software Support2<\/sup><\/td>\nNVIDIA vPC\/vApps, NVIDIA RTX Virtual Workstation (vWS)<\/td>\n<\/tr>\n
vGPU Profiles Supported<\/td>\n4GB, 5 GB, 8GB, 10GB, 20GB, 40GB<\/td>\n<\/tr>\n
NVLink<\/td>\n2-way low profile (2-slot and 3-slot bridges)Connect 2x A800 40GB Active<\/td>\n<\/tr>\n
NVLink Interconnect<\/td>\n400 GB\/s (bidirectional)<\/td>\n<\/tr>\n
Server Options<\/td>\nNVIDIA Certified Systems\u2122 with 1-8 GPUs<\/td>\n<\/tr>\n
NVIDIA AI Enterprise<\/td>\nIncluded3<\/sup><\/td>\n<\/tr>\n
Power Connector<\/td>\n1x PCIe CEM5 16-pin<\/td>\n<\/tr>\n
NVDEC<\/td>\n5x decode<\/td>\n<\/tr>\n<\/tbody>\n<\/table>","protected":false},"featured_media":1641,"comment_status":"open","ping_status":"closed","template":"","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0},"product_brand":[],"product_cat":[49,33],"product_tag":[],"class_list":["post-1640","product","type-product","status-publish","has-post-thumbnail","product_cat-gpu","product_cat-uncategorized","first","instock","sale","taxable","shipping-taxable","purchasable","product-type-simple"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t