V100 16GB SXM2 GPU – Deep Learning & AI Acceleration Card

NVIDIA Tesla V100 16GB SXM2 – Professional GPU Acceleration Card

Unleash next-level computing performance with the V100 16GB SXM2 Graphics Card — one of the most capable data center GPUs ever engineered. Purpose-built for deep learning, AI training, high-performance computing (HPC), and scientific simulation, this powerhouse GPU delivers outstanding throughput for even the most demanding workloads. Whether you're training large neural networks, running parallel compute tasks, or accelerating data analytics pipelines, the V100 SXM2 is built to handle it all at exceptional speed.

Made in Mainland China.

Key Features

• 16GB HBM2 Memory: Ultra-high bandwidth memory designed for large-scale model training and inference tasks with massive datasets.
• SXM2 Form Factor: High-bandwidth NVLink interface for multi-GPU configurations, enabling faster GPU-to-GPU data transfer than standard PCIe connections.
• Tensor Core Technology: Accelerates mixed-precision matrix operations for dramatically faster deep learning training and inference cycles.
• FP64 / FP32 / FP16 Precision Support: Flexible precision modes for scientific computing, simulation, and AI workloads.
• 900 GB/s Memory Bandwidth: Industry-leading memory throughput to keep your GPU fed with data and minimize bottlenecks.
• Ideal for Data Centers & Workstations: Designed for professional server and workstation environments requiring sustained high-performance compute.
• CUDA-Compatible: Full compatibility with NVIDIA's CUDA ecosystem, cuDNN, TensorFlow, PyTorch, and other leading AI/ML frameworks.
• 300W TDP: Enterprise-grade thermal design for sustained compute workloads in rack-mounted server environments.

Who Is This For?

The V100 16GB SXM2 is the go-to GPU for AI researchers, data scientists, ML engineers, HPC professionals, and enterprise IT teams who need reliable, high-throughput compute acceleration. It excels in:

• Training and fine-tuning large language models (LLMs) and deep neural networks
• Computer vision and image recognition workloads
• Computational fluid dynamics and molecular dynamics simulation
• Financial modeling and quantitative analysis
• Rendering and GPU-accelerated database workloads

Technical Specifications

• Model: V100 SXM2
• Memory: 16GB HBM2
• Memory Bandwidth: 900 GB/s
• Form Factor: SXM2
• Interconnect: NVLink
• CUDA Cores: 5,120
• Tensor Cores: 640
• TDP: 300W
• Precision: FP64, FP32, FP16, INT8
• Origin: Mainland China

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For more information on GPU computing standards and high-performance computing best practices, visit the U.S. Department of Energy – Advanced Scientific Computing Research.

Learn more: Graphics card

Frequently Asked Questions

Q: What is the V100 16GB SXM2 GPU used for?

A: The V100 16GB SXM2 is a professional-grade GPU acceleration card designed for deep learning model training, AI inference, high-performance computing (HPC), scientific simulation, and data analytics. It is widely used in data centers, research institutions, and professional workstations requiring massive compute throughput.

Q: What is the difference between the SXM2 and PCIe versions of the V100?

A: The SXM2 version uses NVIDIA's NVLink interconnect, which provides significantly higher GPU-to-GPU bandwidth compared to standard PCIe. This makes the SXM2 form factor ideal for multi-GPU server configurations where fast inter-GPU communication is critical for large-scale model training.

Q: Is the V100 SXM2 compatible with popular AI frameworks like TensorFlow and PyTorch?

A: Yes. The V100 SXM2 is fully compatible with NVIDIA's CUDA ecosystem, including cuDNN, TensorFlow, PyTorch, MXNet, and other leading AI and machine learning frameworks. Its Tensor Core architecture is specifically optimized to accelerate mixed-precision training in these environments.

Q: What kind of system is required to use the V100 SXM2?

A: The V100 SXM2 is designed for server-grade platforms that support the SXM2 form factor, such as the NVIDIA DGX-1 and compatible HPC server chassis. It requires a 300W power delivery solution and adequate cooling for sustained compute workloads. It is not compatible with standard consumer desktop PCIe slots.