NVIDIA A30 24GB PCIe GPU

The NVIDIA A30 24GB PCIe GPU is built on the NVIDIA Ampere architecture, delivering a balanced combination of FP32, FP64, TF32, and BF16 compute performance alongside third-generation Tensor Cores that accelerate both AI inference and mainstream HPC workloads. With 24 GB of HBM2 memory and a 933 GB/s memory bandwidth, the A30 provides the capacity and throughput required for large model inference, scientific simulation, and data analytics pipelines without the thermal and power demands of full-scale HPC accelerators.

Designed for mainstream datacenter density, the A30 operates within a 165 W TDP and fits standard PCIe 4.0 x16 slots, enabling straightforward integration into existing server infrastructure without specialized power delivery or exotic cooling. NVIDIA Multi-Instance GPU (MIG) technology allows the A30 to be partitioned into up to four independent, isolated GPU instances, giving IT administrators the flexibility to serve multiple workloads or tenants from a single physical card while maintaining quality-of-service guarantees.

The A30 supports NVIDIA NVLink for peer-to-peer GPU communication when paired in supported configurations, and is compatible with the full NVIDIA software ecosystem including CUDA, cuDNN, TensorRT, and NVIDIA AI Enterprise. Its position in the NVIDIA data center portfolio targets organizations deploying mainstream AI inference, virtual workstation consolidation, and moderate HPC tasks where the full A100 represents excess capacity, making it a pragmatic choice for enterprise IT buyers seeking to maximize per-GPU utilization across mixed workload environments.

Ideal for

• AI inference serving for natural language processing, recommendation systems, and computer vision models in production datacenter environments
• Mainstream HPC simulation workloads including computational fluid dynamics, molecular dynamics, and finite element analysis requiring FP64 and FP32 double-precision compute
• Multi-tenant GPU virtualization via MIG partitioning, enabling isolated GPU slices for multiple departments or application streams from a single installed card
• Data analytics acceleration for large-scale ETL pipelines, in-database analytics, and GPU-accelerated query processing using frameworks such as RAPIDS
• Training and fine-tuning of small-to-medium scale deep learning models where on-card memory capacity and Tensor Core throughput balance cost-efficiency with iteration speed
• Consolidated virtual workstation and remote visualization delivery for engineering, media, and scientific users requiring GPU-backed professional graphics and compute in a shared infrastructure

Technical specifications

Available from Omnixon Global. Submit an RFQ and our team will confirm configuration and availability for your order.