What are the data throughput rates of V100 GPUs?

The NVIDIA Tesla V100 GPU offers a peak memory bandwidth of up to 900 GB/s, powered by 16GB or 32GB of high-bandwidth HBM2 memory. It features NVLink technology providing up to 300 GB/s interconnect bandwidth between GPUs, enabling ultra-fast data transfer for high-performance AI, deep learning, and HPC workloads. Cyfuture Cloud optimizes this throughput for unparalleled GPU performance in cloud environments.

Overview of NVIDIA Tesla V100 GPU

The NVIDIA Tesla V100 is a data center GPU based on the Volta architecture and designed for deep learning, AI, and high-performance computing. It features up to 5120 CUDA cores and 640 Tensor cores, delivering tremendous computation power along with high-speed memory and data transfer capabilities.

Memory Bandwidth and Throughput

One of the critical specifications of the V100 is its high memory bandwidth due to its use of 2nd generation High Bandwidth Memory (HBM2). The V100 provides:

Peak memory bandwidth: up to 900 GB/s

Memory configurations: 16GB or 32GB of HBM2

Effective bandwidth usage of over 95% in practical workloads

This bandwidth enables the GPU to handle large data sets and complex neural network models efficiently without the memory becoming a bottleneck.​

NVLink Interconnect Bandwidth

The V100 GPU supports NVIDIA's second-generation NVLink technology, which allows multiple GPUs to be interconnected with high-speed links:

6 NVLink connections per GPU

Up to 25 GB/s per NVLink connection

Total combined inter-GPU bandwidth up to 300 GB/s

This NVLink bandwidth is crucial for scaling AI workloads across multiple V100 GPUs as it provides ultra-fast communication between GPUs for distributed training and inference tasks, reducing latency and enabling better scalability.​

Real-World Data Throughput Performance

Practical throughput depends on workload and batch sizes, but benchmarks for V100 show:

Training throughput of models like ResNet-50 can reach over 1500 images/sec with batch size 256 in mixed precision

GPUs deliver 32X training throughput improvements compared to CPU-only systems

Offline throughput for image tasks can vary from several hundred to thousands per second depending on batch size and resolution

Data input throughput from storage to GPU memory can vary, but the internal bandwidth of memory and NVLink ensures data is transferred swiftly once in the processing pipeline.​

Cyfuture Cloud Advantage with V100 GPUs

At Cyfuture Cloud, the Tesla V100 GPU is deployed in a fine-tuned cloud infrastructure that maximizes throughput and efficiency:

Optimized hardware and software stack to achieve peak 900 GB/s memory bandwidth and full NVLink utilization

Scalable GPU resources allowing flexible configurations according to project needs

Specialized technical support team focused on maximizing V100 GPU performance

Secure cloud protocols ensuring data protection during transfer and processing

Offering a high-performance GPU cloud environment with low latency and high-speed connectivity for AI, machine learning, and HPC workloads

Cyfuture Cloud delivers the full power of NVIDIA Tesla V100 throughput rates, making it a top choice for compute-intensive tasks.​

Follow-up Questions and Answers

Q: What workloads benefit most from V100’s high throughput?
A: AI training, deep learning model inference, scientific simulations, high-performance computing, and large-scale data analytics benefit significantly from V100’s memory and interconnect bandwidth.​

Q: How does V100 compare with newer GPUs in throughput?
A: V100 remains powerful, but newer GPUs like the A100 offer higher theoretical bandwidth and tensor performance. However, V100 excels in cost-performance balance in many enterprise scenarios.​

Q: Can V100 GPUs be used in multi-GPU server setups?
A: Yes, V100 supports multi-GPU configurations with NVLink, allowing up to 8 GPUs interconnected with ultra-high bandwidth of 300 GB/s total, enhancing parallel processing.​

Conclusion

The NVIDIA Tesla V100 GPU provides an exceptional data throughput capability with up to 900 GB/s memory bandwidth and 300 GB/s NVLink interconnect bandwidth. These specifications enable high-speed data transfers essential for demanding AI, deep learning, and HPC workloads. Cyfuture Cloud leverages this technology to offer optimized GPU cloud services that maximize throughput performance and scalability, supported by expert technical assistance and secure infrastructure.