IBM EC4L Tesla V100 32GB NVlink GPU

Overview

The IBM EC4L Tesla V100 32GB NVLink GPU is a high-performance accelerator designed for demanding AI, deep learning and scientific computing workloads. Built on NVIDIA's Volta architecture, this GPU delivers massive parallel processing power while maintaining energy efficiency. With 32 gigabytes of HBM2 memory and native NVLink connectivity, the Tesla V100 provides fast data movement between multiple GPUs, enabling large scale model training and complex simulations to run faster than ever.

Key Features

• 32 GB high bandwidth memory (HBM2) for large datasets and models
• 5120 CUDA cores delivering up to 14 teraflops of double precision performance
• 640 Tensor cores optimized for mixed-precision AI workloads
• NVLink interconnect supports up to 300 GB/s bidirectional bandwidth per link
• PCIe Gen3 x16 interface with optional SXM2 form factor for high density servers
• Enhanced error correction and ECC support for mission critical applications
• Integrated NVIDIA cuDNN, NCCL and CUDA libraries for easy software integration

Technical Specifications

• Manufacturer: IBM
• Model / Part Number: EC4L
• GPU Architecture: NVIDIA Volta
• CUDA Cores: 5120
• Tensor Cores: 640
• Memory Capacity: 32 GB HBM2
• Memory Bandwidth: 900 GB/s
• Peak Double Precision Performance: 7 TFLOPS
• Peak Single Precision Performance: 14 TFLOPS
• Peak Tensor Performance: 112 TFLOPS (FP16)
• NVLink Links: Up to 4 per GPU, each 25 GB/s per direction
• Power Consumption: 300 watts TDP
• Form Factor: SXM2 module (compatible with IBM Power servers) or PCIe card version
• Operating Temperature Range: 0 to 85 deg C
• Supported APIs: CUDA, OpenCL, DirectML, Vulkan Compute

Applications

The Tesla V100 is ideal for a wide range of high-end compute tasks. It excels in deep learning training and inference, enabling faster convergence for neural networks in computer vision, natural language processing and recommendation systems. Researchers use it for molecular dynamics, climate modeling and quantum chemistry simulations that require massive parallelism and large memory footprints. In enterprise environments the GPU accelerates data analytics, financial risk modelling and real-time video transcoding, delivering lower latency and higher throughput.