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 optimised 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 centres. 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, op­tim­ising resource util­isa­tion 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 util­isa­tion 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.

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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, vir­tu­al­isa­tion or use in high-per­form­ance data centres, 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 optimised 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 analyse 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.
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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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