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.

Cloud Pricing
Cheapest on Verda — 78% below avgPrices updated daily. Last check: 4/8/2026
Performance
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
About A100 SXM
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.