How Many CUDA Cores Are in the V100 GPU?

The NVIDIA V100 GPU features 5,120 CUDA cores, enabling exceptional parallel processing for AI, deep learning, and high-performance computing workloads.​

What is the NVIDIA V100 GPU?

The NVIDIA V100, built on the revolutionary Volta architecture, represents a milestone in data center GPUs launched in 2017. Designed for intensive AI training, scientific simulations, and HPC applications, it introduced Tensor Cores for accelerated deep learning. Available in 16GB or 32GB HBM2 memory variants, the V100 delivers up to 900 GB/s bandwidth, making it ideal for enterprises scaling complex models.​

Cyfuture Cloud offers V100 GPUs in flexible cloud configurations, allowing seamless deployment without hardware investments.

CUDA Cores Explained

CUDA (Compute Unified Device Architecture) cores are the fundamental parallel processing units in NVIDIA GPUs. Each core handles floating-point and integer operations simultaneously, excelling in matrix multiplications critical for AI and simulations. In the V100, 5,120 CUDA cores provide massive throughput—up to 15 TFLOPS FP32 performance—far surpassing CPU capabilities for parallel tasks.​

These cores work alongside 640 Tensor Cores, boosting mixed-precision training by 12x over previous generations.

V100 Specifications Overview

Cyfuture Cloud optimizes V100 clusters for distributed training, supporting up to 64+ GPUs per workload.​

Performance and Use Cases

With 5,120 CUDA cores, the V100 excels in training large language models, drug discovery simulations, and climate modeling. It replaces up to 135 CPU nodes in HPC environments, offering 30% better FP32 performance than the Pascal P100. Enterprises use it for generative AI, inference, and multi-GPU scaling via NVLink.​

On Cyfuture Cloud, V100s integrate with Kubernetes and Kubeflow for efficient resource orchestration.

Follow-Up Questions

Q1: How does V100 compare to H100 GPUs?
A: V100's 5,120 CUDA cores lag behind H100's 16,896, but V100 remains cost-effective for legacy AI workloads with strong Hopper architecture support.​

Q2: Is V100 suitable for cloud deployment?
A: Yes, Cyfuture Cloud provides on-demand V100 instances, scaling from single GPUs to clusters without upfront costs.​

Q3: What memory options exist for V100?
A: 16GB or 32GB HBM2, with 4096-bit interface for high-bandwidth AI tasks.​

Q4: Can V100 handle distributed training?
A: Absolutely, it scales efficiently in multi-GPU setups on Cyfuture Cloud, supporting frameworks like PyTorch.​

Conclusion

The NVIDIA V100 GPU's 5,120 CUDA cores power transformative AI and HPC innovations, and Cyfuture Cloud makes them accessible via reliable, high-performance cloud services. Whether training deep learning models or running simulations, V100 on Cyfuture Cloud delivers unmatched efficiency and scalability for your workloads.​