{"product_id":"nvidia-tesla-a100-80gb-sxm4-gpu-ampere-architecture-80gb-hbm2e-nvlink-mig-support","title":"NVIDIA Tesla A100 80GB SXM4 GPU (Ampere Architecture, 80GB HBM2e, NVLink, MIG Support)","description":"\u003ch1\u003e\u003cstrong\u003eNVIDIA Tesla A100 80 GB SXM4 GPU - Ampere Architecture for AI \u0026amp; HPC | \u003cspan style=\"color: rgb(255, 42, 0);\"\u003ePNs: 699-2G506-0210-300, [future compatible part numbers]\u003c\/span\u003e\u003c\/strong\u003e\u003c\/h1\u003e\n\u003ch2\u003e\u003cstrong\u003eHigh-Performance NVIDIA A100 SXM4 GPU for Data Centers, AI, and High-Performance Computing\u003c\/strong\u003e\u003c\/h2\u003e\n\u003chr\u003e\n\u003cp\u003eThe \u003cstrong\u003eNVIDIA Tesla A100 80 GB SXM4 GPU\u003c\/strong\u003e (Part Numbers: \u003cstrong\u003e699-2G506-0210-300\u003c\/strong\u003e, and equivalent PNs in the A100 80GB SXM4 family) delivers breakthrough acceleration for AI, data analytics, and HPC workloads.\u003c\/p\u003e\n\u003cp\u003eBuilt on the \u003cstrong\u003eAmpere architecture (GA100 GPU)\u003c\/strong\u003e, it features \u003cstrong\u003e80 GB of HBM2e memory\u003c\/strong\u003e, \u003cstrong\u003e2 TB\/s bandwidth\u003c\/strong\u003e, and \u003cstrong\u003e7,000+ CUDA cores\u003c\/strong\u003e, setting the standard for data center performance.\u003c\/p\u003e\n\u003cp\u003eEngineered for \u003cstrong\u003emulti-GPU NVLink scaling\u003c\/strong\u003e, this A100 SXM4 GPU provides up to \u003cstrong\u003e600 GB\/s interconnect bandwidth\u003c\/strong\u003e, enabling massive AI model training and scientific simulations with unmatched efficiency.\u003c\/p\u003e\n\u003cp\u003eWhether you’re powering \u003cstrong\u003eAI research\u003c\/strong\u003e, \u003cstrong\u003edeep learning clusters\u003c\/strong\u003e, or \u003cstrong\u003eHPC environments\u003c\/strong\u003e, the NVIDIA A100 SXM4 delivers consistent performance, scalability, and energy efficiency trusted by leading enterprises and research institutions.\u003c\/p\u003e\n\u003cp\u003e✔️ \u003cstrong\u003eIn Stock \u0026amp; Ready to Ship – Tested \u0026amp; Certified\u003c\/strong\u003e\u003c\/p\u003e\n\u003chr\u003e\n\u003ch3\u003e\n\u003cstrong\u003e⚙️ Product Specifications: NVIDIA Tesla A100 80 GB SXM4 GPU\u003c\/strong\u003e\u003cbr\u003e\u003ca href=\"https:\/\/www.nvidia.com\/content\/dam\/en-zz\/Solutions\/Data-Center\/a100\/pdf\/nvidia-a100-datasheet-us-nvidia-1758950-r4-web.pdf\" target=\"_blank\" title=\"NVIDIA Tesla A100 80GB SXM4 GPU | Specs Sheet\" rel=\"noopener\"\u003e\u003cstrong\u003eCLICK HERE FOR SPECIFICATIONS SHEET\u003c\/strong\u003e\u003c\/a\u003e\n\u003c\/h3\u003e\n\u003ctable style=\"width: 100.036%; height: 470.4px;\"\u003e\n\u003cthead\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003cth style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eSpecification\u003c\/strong\u003e\u003c\/th\u003e\n\u003cth style=\"width: 68.4848%; height: 19.6px;\"\u003e\u003cstrong\u003eDetails\u003c\/strong\u003e\u003c\/th\u003e\n\u003c\/tr\u003e\n\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: 39.2px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 39.2px;\"\u003e\u003cstrong\u003eModel \/ Part Numbers\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 39.2px;\"\u003e699-2G506-0210-300 (and equivalent A100 SXM4 80GB variants)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eArchitecture\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 19.6px;\"\u003eNVIDIA Ampere (GA100 GPU)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eCUDA Cores\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 19.6px;\"\u003e6,912\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eTensor Cores\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 19.6px;\"\u003e432\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eGPU Memory\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 19.6px;\"\u003e80 GB HBM2e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eMemory Bandwidth\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 19.6px;\"\u003e2,039 GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eFP64 Performance\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 19.6px;\"\u003e9.7 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eFP32 Performance\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 19.6px;\"\u003e19.5 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eTF32 (Tensor Float 32)\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 19.6px;\"\u003e156 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 39.2px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 39.2px;\"\u003e\u003cstrong\u003eFP16 \/ BF16 Performance\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 39.2px;\"\u003e312 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eINT8 Tensor Core\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 19.6px;\"\u003e1,248 TOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eForm Factor\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 19.6px;\"\u003eSXM4 module (for GPU-accelerated servers)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eNVLink Bandwidth\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 19.6px;\"\u003eUp to 600 GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eMIG Support\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 19.6px;\"\u003eUp to 7 instances per GPU\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eTDP\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 19.6px;\"\u003e400 W\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eCooling\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 19.6px;\"\u003ePassive (server chassis airflow required)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eDie Size \/ Transistors\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 19.6px;\"\u003e826 mm², ~54 B transistors\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eCertifications\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 19.6px;\"\u003eNVLink, MIG, DGX\/AEP compatible\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eWeight\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 19.6px;\"\u003eApprox. 2 lb (heatsink), 11 lb full module\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eCondition\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 19.6px;\"\u003eUsed \/ Tested\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eAvailability\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 19.6px;\"\u003eIn Stock – Ready to Ship\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003chr\u003e\n\u003ch3\u003e\u003cstrong\u003e❓ Frequently Asked Questions (FAQs)\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003e\u003cstrong\u003eQ1: Which workloads benefit most from the A100 SXM4 GPU?\u003c\/strong\u003e\u003cbr\u003eA: Deep learning model training, AI inference, HPC simulations, and large-scale analytics all leverage the A100’s Ampere Tensor Core performance.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ2: Are there differences between the various A100 SXM4 part numbers?\u003c\/strong\u003e\u003cbr\u003eA: No - all equivalent PNs (e.g., 699-2G506-0210-300 and future variants) share identical specifications and performance. They mainly differ by regional, batch, or OEM labeling.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ3: What’s the cooling requirement?\u003c\/strong\u003e\u003cbr\u003eA: The SXM4 module uses passive cooling and requires robust server chassis airflow for proper thermal performance.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ4: Does it support Multi-Instance GPU (MIG)?\u003c\/strong\u003e\u003cbr\u003eA: Yes, up to seven isolated GPU instances per device for flexible workload partitioning.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ5: Is this compatible with NVIDIA DGX systems?\u003c\/strong\u003e\u003cbr\u003eA: Yes - it’s designed for DGX, AEP, and other enterprise GPU platforms using SXM4 sockets.\u003c\/p\u003e","brand":"NVIDIA","offers":[{"title":"Default Title","offer_id":46731775017121,"sku":"A100-SXM4-80GB","price":148500.0,"currency_code":"BRL","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/2216\/8673\/files\/NVIDIA-Tesla-A100-80GB-SXM4-GPU-Ampere-Architecture-80GB-HBM2e-NVLink-MIG-Support.png?v=1784705541","url":"https:\/\/www.foxti.com.br\/products\/nvidia-tesla-a100-80gb-sxm4-gpu-ampere-architecture-80gb-hbm2e-nvlink-mig-support","provider":"FoxTI","version":"1.0","type":"link"}