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These instances are powered by eight NVIDIA A100 Tensor Core GPUs, each providing 80 GB of high-bandwidth HBM2e memory. As a foundational accelerator for the previous generation of AI, these are versatile instances that deliver excellent performance for mixed-precision workloads. The GPUs are interconnected with NVIDIA NVLink, making them a powerful and proven choice for distributed training. Their configuration offers a cost-effective solution for a wide range of demanding AI and data analytics tasks.

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Specifications

1 Usable storage is less than the raw capacity when configured as RAID. See RAID layout and throughput to learn about RAID configurations and their performance characteristics.

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Primary use cases

AI model training, high-performance inference, and various HPC applications including scientific simulations. Training models up to 40B parameters such as Gemma 4 31B; a standard choice for fine-tuning most mid-sized open-source models. For inference, they can handle models up to 40B, such as Qwen 3.6 35B-A3B, on a per-GPU basis or a single instance of a quantized larger model such as Qwen 3.5 397B-A17B utilizing tensor parallelism.
Last modified on June 23, 2026