NVIDIA A30 Tensor Core GPU (900-21001-0040-000): This is a data center GPU based on the Ampere architecture with 24GB CoWoS HBM2 memory. It uses passive cooling and a PCIe 4.0 x16 interface. The A30 is designed for AI inference, HPC compute, and multi-tenant GPU virtualization. It is not a consumer GPU, not a workstation GPU, and not primarily intended for large-scale AI training.
Engineering Context
The A30 occupies a specific position in NVIDIA's data center GPU lineup: it sits between the T4 (inference-focused, 70W) and the A100 (training-focused, 300-400W). At 165W TDP with 24GB HBM2 providing 933 GB/s bandwidth, the A30 delivers strong inference throughput and supports Multi-Instance GPU (MIG) partitioning into up to 4 isolated instances. This makes it cost-effective for serving multiple AI models simultaneously in shared infrastructure. NVLink 3rd generation (200 GB/s) enables multi-GPU configurations within a server node. The passive heatsink requires a server chassis with adequate directed airflow.
Deployment & Use Cases
AI Inference at Scale: MIG support enables running multiple inference models on a single GPU with guaranteed resources and isolation.
GPU Virtualization (vGPU): Compatible with NVIDIA AI Enterprise and vComputeServer for multi-tenant GPU sharing across VMs.
HPC Compute: FP64 Tensor Core performance (10.3 TF) serves scientific computing workloads requiring double-precision.
Mixed AI Workloads: INT8 (330 TOPS) and FP16 (165 TF) performance supports both computer vision and NLP inference.
Technical Specifications
Model: NVIDIA A30
Part Number: 900-21001-0040-000
Architecture: Ampere
Memory: 24GB CoWoS HBM2 (933 GB/s)
Interface: PCIe 4.0 x16
NVLink: 3rd Generation (200 GB/s)
TDP: 165W
Cooling: Passive (requires server airflow)
Form Factor: FHFL (Full Height, Full Length), Dual-Slot
MIG: 4x 6GB / 2x 12GB / 1x 24GB
FP16: 165 TF (330 TF with sparsity)
INT8: 330 TOPS (661 TOPS with sparsity)
Condition: Refurbished — Tested by T.E.S IT-SOLUTIONS
Compatibility & Hard Constraints
Passive Cooling Only: This SKU has no fan. It requires a server chassis with directed front-to-back airflow. It will thermal-throttle or shut down in a workstation or open bench without forced airflow.
Not for Large-Scale Training: With 24GB HBM2, the A30 cannot hold large language models. For dedicated training, the A100 (40GB or 80GB HBM2e) is required.
PCIe 4.0 Required: Full bandwidth requires PCIe Gen4 x16. The GPU will function in PCIe Gen3 slots but with reduced host-to-GPU transfer throughput.
MIG Driver Requirements: MIG partitioning requires NVIDIA driver 450.80.02 or later with a supported OS. Not all hypervisors support MIG.