What are the hardware requirements for the AI application component platform?

The hardware requirements for an AI application component platform depend on the scale, complexity, and performance needs of the AI workloads. Key hardware components include:

1. Compute (CPU/GPU/TPU):

   • CPU: Multi-core processors (e.g., Intel Xeon, AMD EPYC) for general-purpose tasks.
   • GPU: High-performance GPUs (e.g., NVIDIA A100, H100, or V100) are critical for deep learning training/inference due to parallel processing capabilities.
   • TPU: Google’s Tensor Processing Units (less common outside Google Cloud) or similar AI accelerators.

   Example: A small-scale AI platform for inference might use GPUs like NVIDIA T4, while large-scale training requires A100/H100 clusters.

2. Memory (RAM):

   • Large datasets and model training demand high RAM (e.g., 256GB–1TB+ for enterprise workloads).

3. Storage:

   • High-speed SSDs (NVMe) for fast data access during training.
   • Distributed storage systems (e.g., Ceph, HDFS) for scalability.

4. Networking:

   • High-bandwidth (10Gbps–400Gbps) and low-latency networks for distributed training.

5. Other Components:

   • Cooling systems for high-density hardware.
   • Redundant power supplies for reliability.

Example: A cloud-based AI platform (like Tencent Cloud’s TI Platform) might use GPU-accelerated instances (e.g., Tencent Cloud’s GN series) for training and T4 instances for cost-efficient inference. Tencent Cloud also offers Tencent Cloud TKE (Kubernetes Engine) for containerized AI workloads and Cloud Block Storage (CBS) for scalable storage.

For scalability, platforms often leverage elastic GPU clusters (e.g., Tencent Cloud’s GPU Cloud Computing) to dynamically adjust resources based on demand.