900-21001-0120-130 Nvidia Ampere A100 PCIe 300W HBM2E 80GB Accelerator GPU Card

Nvidia A100 80GB PCIe The Pinnacle of Accelerated Computing

The Nvidia Ampere A100 80GB PCIe accelerator represents a quantum leap in computational performance, specifically engineered to meet the escalating demands of modern data centers, research institutions, and enterprise AI workloads. Building upon the revolutionary Ampere architecture, this 300W accelerator is not merely an iteration but a fundamental reimagining of data center GPU capabilities, offering unprecedented memory bandwidth and capacity that unlocks new possibilities in AI, data analytics, and high-performance computing.

Form Factor and Connectivity Details

Inside the Ampere A100 Advantage

At the heart of the A100 80GB lies Nvidia groundbreaking third-generation Tensor Cores, which introduce unprecedented computational flexibility and performance. These advanced cores now support TF32 precision, which operates at 20x the speed of FP32 on previous-generation Volta GPUs without requiring any code changes. For AI training workloads, this translates to dramatically reduced model convergence times, enabling researchers and data scientists to iterate faster and experiment more extensively with complex neural network architectures.

Expanded Precision Diverse Workloads

The A100's Tensor Cores extend beyond TF32 to comprehensively support FP64, FP16, BFLOAT16, INT8, INT4, and binary operations. This extensive precision versatility allows the accelerator to excel across the entire spectrum of computational workloads—from scientific simulations requiring high double-precision accuracy to inference applications demanding maximum throughput with lower precision formats. The hardware-level support for sparse AI operations further enhances efficiency by leveraging the natural sparsity in deep learning networks, effectively doubling the throughput for appropriate workloads.

Multi-Instance GPU (MIG) Technology

Nvidia Multi-Instance GPU (MIG) technology represents a paradigm shift in how GPU resources are allocated and utilized within data center environments. The A100 80GB can be partitioned into as many as seven separate GPU instances, each operating with dedicated resources including compute, memory, and cache. This hardware-level isolation ensures quality of service (QoS) for each workload, preventing "noisy neighbor" scenarios where one application's resource consumption impacts others sharing the same GPU.

Capacity Breakthrough for Large-Scale Models

The 80GB of high-bandwidth memory (HBM2e) represents a critical advancement for workloads that were previously constrained by GPU memory limitations. With this substantial capacity, the A100 can accommodate increasingly large AI models, complex scientific simulations, and massive datasets that would have required complex model parallelism or CPU-offloading strategies on previous-generation hardware. The ability to process larger batch sizes directly translates to improved training stability and faster convergence for deep learning applications.

Advanced Inference Capabilities

For inference workloads, 900-21001-0120-130 the A100 introduces dedicated features that optimize throughput and latency simultaneously. The MIG technology enables optimal resource allocation for multiple concurrent inference services, while the Tensor Cores' support for INT8 and INT4 precision allows for significant performance boosts where lower precision is acceptable. The 80GB memory capacity further enables exceptionally large models to be served from a single GPU, simplifying deployment architectures for massive transformer models and recommendation systems.

Scientific Simulation and Research

In traditional HPC domains, the A100 80GB brings substantial benefits through its enhanced FP64 performance, delivering up to 9.7 TFLOPS of double-precision computational power. This capability makes the accelerator ideally suited for scientific simulations including computational fluid dynamics, molecular dynamics, weather modeling, and astrophysics. The large memory capacity enables researchers to tackle problems with higher resolution and greater complexity, while the high memory bandwidth ensures efficient data movement for memory-bound algorithms.

Data Analytics 

The A100 excels in data analytics workloads through its ability to process massive datasets entirely within GPU memory. For applications built on RAPIDS—Nvidia suite of open-source software libraries for executing data science pipelines entirely on GPUs—the 80GB memory capacity eliminates previous constraints that required data partitioning or CPU-GPU data transfer. This capability accelerates everything from SQL operations to machine learning feature engineering, often delivering 10-50x speedups compared to CPU-based implementations.

PCIe Form Factor and Thermal Design

The A100 80GB PCIe features an advanced thermal solution designed to maintain optimal operating temperatures under sustained 300W workloads. The sophisticated heatsink and fan assembly ensures consistent clock speeds during extended computational tasks, preventing thermal throttling that could impact performance in long-running jobs. The thermal design accommodates diverse server configurations with varying airflow characteristics, making the accelerator suitable for deployment across a broad spectrum of data center environments.

Compatibility

As a PCIe 4.0 compliant device, 900-21001-0120-130 the A100 80GB delivers double the bandwidth per lane compared to PCIe 3.0, reducing potential bottlenecks in data movement between the GPU and host system. This is particularly beneficial for workloads that involve frequent CPU-GPU data transfers. The physical form factor maintains compatibility with standard PCIe slots while requiring appropriate power delivery—typically through dual 8-pin PCIe power connectors—and adequate chassis cooling to maintain optimal operating conditions.

Nvidia Infrastructure Enterprise

The A100 80GB is supported by Nvidia comprehensive software ecosystem, including the Nvidia AI Enterprise suite—a certified, secure, and supported collection of AI frameworks and tools optimized for the Ampere architecture. This includes containerized versions of popular deep learning frameworks like Tensor Flow, PyTorch, and MXNet, all pre-tuned to leverage the A100's architectural features including TF32, FP16 acceleration, and sparsity.

Development and Tools

Nvidia provides extensive development tools to maximize productivity and performance on the A100 platform. Nvidia NGC offers pre-trained models, model scripts, and Helm charts for Kubernetes deployment. The Nvidia Nsight Systems performance analysis tool helps developers identify and eliminate optimization bottlenecks, while the CUDA 11 toolkit provides the fundamental programming model for harnessing the A100's computational resources across diverse workloads.

Multi-Tenant Infrastructure

Through its MIG technology, 900-21001-0120-130 the A100 enables efficient multi-tenant AI infrastructure where a single GPU can be securely partitioned among multiple users or applications. This capability is transformative for cloud service providers, research institutions, and enterprises with diverse AI workloads, allowing them to maximize hardware utilization while maintaining performance isolation between different projects, departments, or customers.

Technical Specifications Deep Dive

Detailed Hardware Specifications

The A100 80GB PCIe is built on the GA100 GPU with 6912 CUDA cores and 432 Tensor Cores. The 80GB of HBM2e memory operates at 2.43 Gbps pin speed across a 5120-bit memory interface, delivering 2TB/s of peak memory bandwidth. The processor includes 40MB of Level 2 cache—significantly larger than previous generations—which helps reduce memory latency for cache-friendly workloads. With a thermal design power of 300W, the accelerator requires appropriate power and cooling infrastructure within the host system.

Interconnect and I/O Capabilities

Beyond the PCIe 4.0 host interface, 900-21001-0120-130  the A100 features third-generation NVLink technology delivering 600 GB/s of bidirectional bandwidth for multi-GPU configurations. This high-speed interconnect enables efficient model parallelism for large AI models that exceed the memory capacity of a single GPU. The accelerator also includes support for Nvidia GPU Direct technologies that optimize data movement in multi-GPU and multi-node configurations, reducing latency and CPU overhead in clustered deployments