The NVIDIA A30 is a flexible server GPU that offers compute acceleration for a wide range of enterprise workloads. It was specially developed for AI inference, deep learning and high-performance computing (HPC), but is also suitable for extensive data analysis. With its Tensor Cores, the A30 achieves up to 165 TFLOPS (TeraFLOPS) of deep learning performance and delivers 10.3 TFLOPS for HPC workloads.

What are the performance features of the NVIDIA A30?

The NVIDIA A30 is based on the Ampere architecture, which is part of the EGX platform, through which NVIDIA provides an optimized infrastructure for artificial intelligence and high-performance computing. The A30 is also equipped with the third generation of Tensor Cores, which massively accelerate inference processes and shorten training times. The following overview lists the key performance features of the server GPU:

  • 165 TFLOPS TF32 computing power for deep learning or AI training and inference
  • 10.3 TFLOPS FP64 computing power for HPC applications such as scientific calculations or simulations
  • 10.3 TFLOPS FP32 performance for general calculations
  • 24 gigabytes of HBM2 memory (GPU memory)
  • GPU memory bandwidth of 933 gigabytes per second - optimal for parallel workloads
  • Power consumption: 165 watts
  • PCIe Gen4 with 64 gigabytes per second for fast data transfers
  • NVLINK with 200 gigabytes per second for multi-GPU communication
Note

TFLOPS (Tera Floating Point Operations Per Second) is a unit that describes the processing speed of computers. One TeraFLOPS corresponds to one trillion calculations per second.

What are the advantages and disadvantages of the NVIDIA A30?

The NVIDIA A30 offers a good balance of computing power, energy efficiency and scalability. The most significant advantages of the server GPU include:

  • Cost-efficient computing power: The A30 combines high AI and HPC performance with comparatively low power consumption, ensuring energy-efficient operation in data centers. Due to its good price-performance ratio, it’s ideal for companies that need a powerful GPU but want to avoid high investment costs.
  • Multi-instance GPU (MIG): The NVIDIA A30 can be partitioned into up to four independent GPU instances. This makes it possible to run multiple workloads with high bandwidth and dedicated memory in parallel, optimizing resource utilization and increasing efficiency.
  • Next generation NVLink: NVIDIA NVLink allows two A30 GPUs to be linked together to accelerate larger workloads and provide higher memory bandwidth.
  • Good scalability: Whether smaller workloads or complex calculations, the A30 GPU is suitable for a wide range of requirements. Thanks to MIG functionality, NVLink and PCIe Gen4, it enables flexible resource utilization that can be dynamically adapted to individual requirements.

The weaknesses of the A30 GPU become apparent in comparison with top models such as the NVIDIA H100 or the A100. Although the A30 offers high performance, it cannot quite keep up with high-end GPUs in terms of performance. The NVIDIA A30 also uses HBM2 memory, while more powerful models often already work with the HBM3 standard and therefore have an even higher memory bandwidth.

What areas of application is the NVIDIA A30 best suited to?

The NVIDIA A30 is designed for a wide range of AI and HPC workloads. Whether cloud computing, virtualization or use in high-performance data centers, the A30 is suitable for a wide range of enterprise workloads. The main areas of application include:

  • Deep learning training: The A30 is used for training neural networks. The GPU is particularly well suited to transfer learning (adapting to new data sets) and leaner deep learning models tailored to specific tasks.
  • Inference for deep learning: The GPU is optimized for inference workloads and enables fast, efficient calculations for pre-trained AI models. This makes the NVIDIA A30 ideal for real-time applications such as automatic speech recognition or image analysis.
  • High-performance computing: The A30 GPU can also be used for complex calculations and simulations that require high computing power, such as financial analyses or scientific simulations in the field of weather forecasting. Especially for less demanding HPC workloads, the A30 offers a cost-effective solution.
  • Extensive data analysis: As the GPU can process large amounts of data quickly and analyze it efficiently, the A30 is also used in the areas of big data, business intelligence and machine learning.
  • GPU server: The A30 GPU enables companies to operate powerful GPU servers cost effectively and to scale them as required.

What are possible alternatives to the NVIDIA A30?

Both NVIDIA itself and competitors such as Intel and AMD offer various alternatives to the A30. Within the NVIDIA portfolio, for example, the A100 and the H100 are alternatives that offer an even higher performance level. The AI accelerator Intel Gaudi 3 is primarily designed for inference applications and the AMD Instinct MI210 accelerator is a high-performance alternative from the AMD ecosystem. Detailed information on frequently used graphics processors and AI accelerators can be found in our guide comparing server GPUs.

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