The NVIDIA A30 is an GPU for flexible servers that offers an acceleration of calculation for the most diverse workloads. It was specially designed for IA inference, Deep Learning and High Performance Computing (HPC), but it is also suitable for large -scale data analysis in particular. Thanks to its tensor hearts, the A30 reached up to 165 tflops (teraflops) in Deep Learning and provides 10.3 TFOPS for HPC workloads.
What are the performance of the Nvidia A30?
The NVIDIA A30 is based on AMPERE architecture, which is part of the EGX platform, by which NVIDIA provides an infrastructure optimized for artificial intelligence and high performance computing. The A30 is also equipped of the third generation of tensor heartswhich significantly accelerate the inference processes and reduce training times. The preview below lists the main performance characteristics of this GPU for servers:
- 165 TF32 calculation power tflops For Deep Learning, IA model training and inference
- 10.3 FP64 calculation power tflops For HPC applications such as scientific calculations or simulations
- 10.3 FP32 performance tflops For general calculations
- 24 GB of HBM2 memory dedicated to the GPU
- GPU memory bandwidth of 933 GB/soptimal for parallel workloads
- Electric consumption: 165 watts
- PCIe Gen4 with 64 GB/S For quick data transfers
- NVLink with 200 GB/S For multi-GPU communication
Note
Tflops (Tera Floating Point Operations per second) is a unit used to describe the processing speed of computers. A teraflops corresponds to a billion of calculations per second.
What are the advantages and disadvantages of the Nvidia A30?
The NVIDIA A30 offers a good balance between computing power, energy efficiency and scalability. The most important advantages of this GPU for servers are:
- Profitable calculation power : the A30 combines high performance in AI and HPC with low energy consumption, guaranteeing effective energy exploitation in data centers. Thanks to its good quality/price ratio, it is perfectly suitable for companies that need an efficient GPU, but who want to avoid high investment costs.
- Multi-instance GPU (MIG): The NVIDIA A30 can be partitioned as much as possible in four independent GPU instances. It is thus possible to carry out several workloads in parallel with a high bandwidth and clean memory, which optimizes the use of resources and increases efficiency.
- New generation NVLink : Thanks to NVIDIA NVLink, two A30 GPUs can be linked together to speed up important workloads and provide a larger memory bandwidth.
- Good scalability : Whether it is small workloads or complex and intensive calculations, the A30 GPU is suitable for the most diverse requirements. Thanks to the MIG, NVLink and PCIE Gen4 functionality, it allows flexible use of resources, dynamically adapted to needs.
The weaknesses of the A30 GPU prove to be when compared with advanced models such as the Nvidia H100 or the A100. The A30 certainly offers high performance, but it cannot completely compete with high -end GPUs in terms of power. Another drawback is that the NVIDIA A30 uses HBM2 memory, while more powerful models often work with the HBM3 standard and thus have an even higher memory bandwidth.
Areas of application: where is the NVIDIA A30 the most suitable?
The NVIDIA A30 is designed for a wide range of IA and HPC workloads. Whether cloud computing, virtualization or use in high -performance calculation centers, the A30 is suitable for business workloads of all types. Its main areas of application are:
- Deep Learning training : The A30 is used for training neural networks. The GPU is particularly suitable for Transfer Learning (Adaptation to new data sets) and lighter Deep Learning models, designed for specific tasks.
- Inference for Deep Learning : the GPU is optimized for inference workloads and allows rapid and effective calculations for pre-trained AI models. The NVIDIA A30 is therefore ideal for real -time applications such as voice recognition or image analysis.
- High Performance Computing : The A30 GPU can also be used for complex calculations and simulations that require a great computing power, such as financial analyzes or scientific simulations for weather forecasts. For the less demanding HPC workloads in particular, the A30 offers a profitable solution.
- Large -scale data analysis : thanks to its ability to treat and analyze quickly, the A30 is also used in the fields of Big databusiness intelligence and Machine learning.
- GPU server : The GPU A30 allows companies to deploy PPI servers at a reduced cost and to be able to develop them according to needs.
GPU servers
Hardware dedicated with a powerful graphics card
Use the GPU calculation power in all flexibility to manage large amounts of data and only pay the resources used.
What are the alternatives to the Nvidia A30?
Nvidia itself as well as competing companies like Intel or AMD offer different alternatives to the A30. In the NVIDIA portfolio, the A100 and the H100 are thus alternatives even more efficient. The Intel Gaudi 3 artificial intelligence accelerator is mainly designed for inference applications and the AMD Instinct MI210 accelerator represents a high performance option of the AMD ecosystem. You will find detailed information on the most common GPUs and Accelerators IA used in our GPU comparison guide for servers.
