Training AI models for next-level challenges such as conversational AI requires massive compute power and scalability.
NVIDIA A30 Tensor Cores with Tensor Float (TF32) provide up to 10X higher performance over the NVIDIA T4 with zero code changes and an additional 2X boost with automatic mixed precision and FP16, delivering a combined 20X throughput increase. When combined with NVIDIA® NVLink®, PCIe Gen4, NVIDIA networking, and the NVIDIA Magnum IO™ SDK, it’s possible to scale to thousands of GPUs.
Tensor Cores and MIG enable A30 to be used for workloads dynamically throughout the day. It can be used for production inference at peak demand, and part of the GPU can be repurposed to rapidly re-train those very same models during off-peak hours.
NVIDIA set multiple performance records in MLPerf, the industry-wide benchmark for AI training.
Learn more about the NVIDIA Ampere architecture for training ›