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NVIDIA Ampere Architecture · GA100

NVIDIA A100 PCIe 80GB
80 GB HBM2e · 1,935 GB/s

The NVIDIA A100 PCIe 80GB introduced third-generation Tensor Cores, Multi-Instance GPU (MIG) partitioning, and 80GB HBM2e — setting the benchmark for enterprise AI infrastructure that remains widely deployed for AI inference and HPC workloads.

All specifications from NVIDIA A100 datasheet DS-10010-001. No pricing displayed — contact Servnet for UK availability.

NVIDIA A100 PCIe 80GB GPU accelerator card

NVIDIA A100 PCIe 80GB. Image: Wikimedia Commons (Nvidia Tesla A100.png). Specifications per NVIDIA DS-10010-001.

80 GB
HBM2e VRAM
1,935 GB/s
Memory bandwidth
312 TFLOPS
FP16 Tensor Core
6,912
CUDA Cores
7× MIG
Instances at 10GB
PCIe 4.0
x16 host interface
Technical Specifications

NVIDIA A100 PCIe 80GB — Full Datasheet Specifications

All figures from NVIDIA A100 datasheet (DS-10010-001). Tensor Core TFLOPS with sparsity assumes 2:4 structured sparse format. Note: A100 does not support FP8 — that was introduced with Hopper (H100).

Architecture
GPU ArchitectureNVIDIA Ampere (GA100)
CUDA Cores6,912
Tensor Cores432 (3rd-generation)
Process NodeTSMC 7nm N7
Transistors54.2 billion
Memory
VRAM80 GB HBM2e
Memory Bandwidth1,935 GB/s
Memory Bus Width5,120-bit
ECCYes — full memory ECC
Compute Performance
FP64 Tensor Core19.5 TFLOPS
FP32 (CUDA)19.5 TFLOPS
TF32 Tensor Core156 TFLOPS (312 TFLOPS with sparsity)
FP16 Tensor Core312 TFLOPS (624 TFLOPS with sparsity)
BF16 Tensor Core312 TFLOPS (624 TFLOPS with sparsity)
INT8 Tensor Core624 TOPS (1,248 TOPS with sparsity)
FP8Not supported (Ampere architecture)
Connectivity
Form FactorPCIe — dual-slot, FHFL (full-height, full-length)
Host InterfacePCIe 4.0 x16 — 64 GB/s bidirectional
NVLink600 GB/s (NVLink Bridge for 2× A100 pair)
Display OutputsNone (data-centre headless)
Platform
Multi-Instance GPUYes — up to 7 MIG instances at 10GB each
NVIDIA AI EnterpriseSupported (CUDA, TensorRT, Triton)
TDP300W
CoolingPassive (requires system-level airflow)
Power Connector16-pin (1× adapter)

Source: NVIDIA A100 80GB PCIe Datasheet DS-10010-001. All trademarks are the property of NVIDIA Corporation.

PCIe GPU Comparison

A100 vs H100 vs H200 PCIe

GPU ModelVRAMBandwidthFP16 TFLOPSPCIe GenTDP
THIS PAGEA100 PCIe 80GB
80GB HBM2e1.9 TB/s312 TFLOPS4.0300W
H100 PCIe
80GB HBM2e2.0 TB/s1,671 TFLOPS5.0350W
H200 NVL PCIe
141GB HBM3e4.8 TB/s1,979 TFLOPS5.0350W

H100 FP16 figure includes sparsity. A100 FP16 without sparsity: 312 TFLOPS. Source: NVIDIA datasheets.

Applications

A100 PCIe Key Workloads

The A100 Ampere architecture introduced third-generation Tensor Cores supporting TF32 (new in Ampere) and BF16 precision for AI, and introduced MIG — GPU partitioning features that remain commercially relevant for inference infrastructure.

AI Inference (Production)

80GB VRAM handles inference of models up to ~30B parameters at FP16. MIG partitioning enables up to 7 concurrent isolated inference services on one card — ideal for multi-tenant API hosting.

🧠

Deep Learning Training

TF32 Tensor Cores deliver 156 TFLOPS for standard precision and 312 TFLOPS with sparsity — accelerating ResNet, BERT, GPT-2 scale models and scientific neural networks.

🔀

Multi-Instance GPU (MIG)

A100 introduced MIG — the first GPU to support guaranteed hardware partitioning into independent GPU instances. Seven 10GB instances enable isolated training, inference, or analytics workloads per tenant.

🔬

HPC & Scientific Simulation

19.5 TFLOPS FP64 Tensor Core and 80GB memory capacity with NVLink bridge support accelerate computational chemistry, bioinformatics, seismic processing, and CFD simulations.

💡

Cost-Optimised Inference

The A100 PCIe remains relevant for inference where H100 supply is constrained or capital budgets are tighter. Its 80GB VRAM and MIG support provide production-grade multi-workload capability.

📊

RAPIDS GPU Analytics

cuDF, cuML, and RAPIDS Accelerator for Apache Spark use A100's HBM2e bandwidth (1,935 GB/s) to deliver 2–10x throughput improvement for in-memory analytics versus CPU-only clusters.

Compare & Related Products

Need NVIDIA A100 PCIe for AI inference or HPC?

Servnet supplies A100 PCIe 80GB for UK enterprise and research deployments. We advise on upgrade path to H100/H200 PCIe, confirm server compatibility (PCIe 4.0 x16, 300W requirement), and provide current UK availability.

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