ultra

A100 SXM GPU

The A100 SXM provides higher memory bandwidth and faster GPU-to-GPU communication via NVLink, making it better suited for multi-GPU AI training and HPC clusters. It handles large models and datasets more efficiently than PCIe variants.

VRAM 80GB
CUDA Cores 6,912
Tensor Cores 432
TDP 400W
Process 7nm
From
$0.45/hr
across 18 providers
A100 SXM GPU

Cloud Pricing

Cheapest on Verda 78% below avg
ProviderGPUsPrice / hrUpdatedSource
1× GPU
$0.45
4/8/2026
2× GPU
$0.45
4/8/2026
4× GPU
$0.45
4/8/2026
8× GPU
$0.45
4/8/2026
1× GPU
36mo$0.69
4/5/2026
1× GPU
$0.79
4/8/2026
1× GPU
24mo$0.79
4/5/2026
2× GPU
24mo$0.79
4/5/2026
1× GPU
6mo$0.84
3/30/2026
1× GPU
$0.89
4/8/2026
1× GPU
3mo$0.89
3/30/2026
1× GPU
12mo$0.89
4/5/2026
2× GPU
12mo$0.89
4/5/2026
1× GPU
1mo$0.94
3/30/2026
1× GPU
6mo$0.99
4/5/2026
2× GPU
6mo$0.99
4/5/2026
1× GPU
$1.09
4/5/2026
2× GPU
$1.09
4/5/2026
1× GPU
$1.14
4/8/2026
1× GPU
36mo$1.15
4/6/2026
1× GPU
$1.28
4/8/2026
2× GPU
$1.28
4/8/2026
4× GPU
$1.28
4/8/2026
8× GPU
$1.28
4/8/2026
2× GPU
$1.29
4/8/2026
4× GPU
$1.29
4/8/2026
1× GPU
$1.29
4/8/2026
2× GPU
$1.29
4/8/2026
4× GPU
$1.29
4/8/2026
8× GPU
$1.29
4/8/2026
2× GPU
$1.30
4/8/2026
1× GPU
$1.30
3/31/2026
1× GPU
$1.35
4/8/2026
2× GPU
$1.35
4/8/2026
4× GPU
$1.35
4/8/2026
8× GPU
$1.35
4/8/2026
1× GPU
$1.38
4/8/2026
8× GPU
$1.38
4/8/2026
2× GPU
36mo$1.38
4/5/2026
1× GPU
$1.39
4/8/2026
2× GPU
$1.42
4/8/2026
4× GPU
$1.42
4/8/2026
1× GPU
$1.45
3/30/2026
1× GPU
$1.45
4/8/2026
2× GPU
$1.45
4/8/2026
4× GPU
$1.45
4/8/2026
1× GPU
$1.49
4/6/2026
8× GPU
$1.65
4/8/2026
1× GPU
$1.80
4/7/2026
1× GPU
36mo$1.95
3/30/2026
1× GPU
$1.99
4/8/2026
8× GPU
$1.99
4/8/2026
1× GPU
24mo$2.00
3/30/2026
1× GPU
12mo$2.10
3/30/2026
1× GPU
6mo$2.30
3/30/2026
1× GPU
3mo$2.50
3/30/2026
1× GPU
$2.59
4/8/2026
4× GPU
$2.59
4/8/2026
8× GPU
$2.59
4/8/2026
1× GPU
$2.60
4/8/2026
8× GPU
$2.70
4/7/2026
8× GPU
$2.74
4/8/2026
4× GPU
36mo$2.76
3/31/2026
1× GPU
1mo$2.80
3/30/2026
1× GPU
$3.06
3/30/2026
4× GPU
$3.11
4/8/2026
2× GPU
$3.13
4/8/2026
4× GPU
24mo$3.16
3/31/2026
8× GPU
$3.18
4/8/2026
4× GPU
12mo$3.56
3/31/2026
4× GPU
6mo$3.96
3/31/2026
4× GPU
$4.36
3/31/2026
8× GPU
36mo$5.52
3/31/2026
8× GPU
24mo$6.32
3/31/2026
8× GPU
12mo$7.12
3/31/2026
8× GPU
6mo$7.92
3/31/2026
8× GPU
$8.72
3/31/2026
Direct from providerVia marketplace

Prices updated daily. Last check: 4/8/2026

Performance

FP16
312 TFLOPS
FP32
19.5 TFLOPS
BF16
312 TFLOPS
INT8
624 TOPS
Bandwidth
2039 GB/s

Strengths & Limitations

  • 80GB HBM2e memory capacity supports large models and datasets
  • Multi-Instance GPU (MIG) technology enables partitioning into seven independent instances
  • 2,039 GB/s memory bandwidth provides high data throughput
  • 432 third-generation Tensor Cores accelerate AI and ML workloads
  • TF32 precision support improves AI training performance without code changes
  • NVLink connectivity enables high-speed multi-GPU scaling
  • SXM form factor supports 400W power envelope for maximum performance
  • 400W power consumption requires substantial cooling infrastructure
  • Previous-generation architecture compared to newer H100 and GB300 series
  • Higher power draw than PCIe variants increases operational costs
  • Limited to NVIDIA CUDA ecosystem for GPU-accelerated computing
  • May be overkill for simple inference tasks that don't require 80GB memory

Key Features

NVIDIA Ampere Architecture
Multi-Instance GPU (MIG) Technology
Third-generation Tensor Cores
TF32 precision support
NVLink high-speed interconnect
HBM2e memory technology
CUDA Cores with enhanced compute capability
NVSwitch compatibility for multi-GPU systems

About A100 SXM

The NVIDIA A100 SXM is a data center GPU based on the Ampere architecture, representing NVIDIA's previous-generation offering before the newer H100 and GB300 series. Built on a 7nm manufacturing process, the A100 SXM delivers substantial computational power with 80GB of HBM2e memory and 6,912 CUDA cores alongside 432 third-generation Tensor Cores. The SXM form factor enables higher power delivery at 400W TDP compared to PCIe variants, supporting more demanding workloads in server environments. Key technical specifications include 2,039 GB/s of memory bandwidth through a 5,120-bit memory interface, 312 TFLOPS of FP16 performance, and 624 TOPS for INT8 operations. The A100 SXM incorporates Multi-Instance GPU (MIG) technology, allowing partitioning into up to seven independent GPU instances, and supports TF32 precision for enhanced AI training efficiency. NVLink connectivity provides high-speed inter-GPU communication for multi-GPU configurations. In cloud deployments, the A100 SXM serves AI training workloads, deep learning inference, and high-performance computing applications that require substantial memory capacity and computational throughput. The 80GB memory configuration accommodates large language models and complex datasets, while MIG capability enables multi-tenant scenarios where GPU resources need isolation and dedicated allocation.

Common Use Cases

The A100 SXM is well-suited for AI training workloads requiring substantial memory capacity, particularly large language models and computer vision tasks that benefit from the 80GB memory configuration. Deep learning inference applications with high throughput requirements can leverage the 312 TFLOPS FP16 performance and 624 TOPS INT8 capability. High-performance computing applications in scientific research, financial modeling, and data analytics benefit from the combination of CUDA cores and memory bandwidth. Multi-tenant cloud environments can utilize MIG technology to partition the GPU into smaller instances, maximizing resource utilization while maintaining workload isolation.

Full Specifications

Hardware

Manufacturer
NVIDIA
Architecture
Ampere
CUDA Cores
6,912
Tensor Cores
432
RT Cores
0
Process Node
7nm
TDP
400W

Memory & Performance

VRAM
80GB
Memory Interface
5120-bit
Memory Bandwidth
2039 GB/s
FP32
19.5 TFLOPS
FP16
312 TFLOPS
BF16
312 TFLOPS
FP64
9.7 TFLOPS
INT8
624 TOPS
Release
2020

Frequently Asked Questions

How much does an A100 SXM cost per hour in the cloud?

A100 SXM pricing varies by provider, region, and commitment level. Check the pricing table above for current rates across all providers.

What is the A100 SXM best used for?

The A100 SXM excels at AI training and inference workloads requiring large memory capacity, particularly large language models and deep learning applications. The 80GB memory configuration and high bandwidth make it suitable for complex datasets, while MIG technology enables efficient multi-tenant deployments.

How does the A100 SXM compare to the H100 for AI workloads?

The H100 represents a newer generation with improved Transformer Engine capabilities and higher performance per watt. However, the A100 SXM still provides substantial computational power with 80GB memory and proven compatibility across AI frameworks, making it a viable option for workloads that don't require the latest architectural improvements.