To build a DeepSeek model application, several key hardware resources are required, primarily focusing on computational power, memory, and storage. Here's a breakdown:
GPUs (Graphics Processing Units): Deep learning models like DeepSeek rely heavily on GPUs for training and inference due to their parallel processing capabilities. High-performance GPUs such as NVIDIA A100, H100, or V100 are ideal for accelerating matrix operations and large-scale neural network training. For smaller-scale deployments, mid-range GPUs like NVIDIA T4 or RTX 30/40 series can be used.
CPU (Central Processing Unit): While GPUs handle most of the heavy lifting, CPUs are still essential for data preprocessing, orchestration, and running supporting services. A multi-core CPU (e.g., AMD EPYC or Intel Xeon) with high clock speeds ensures smooth workflow management.
Memory (RAM): Large models require significant RAM for loading datasets, intermediate computations, and model parameters. For training, 256GB or more is recommended, while inference can work with 64GB–128GB depending on the model size.
Storage: Fast and scalable storage is crucial for datasets, model checkpoints, and logs. NVMe SSDs (e.g., Samsung PM9A3 or Intel Optane) provide low-latency access, while distributed file systems (like Ceph or Tencent Cloud’s CFS) help manage large-scale data.
Networking: High-bandwidth networking (10Gbps or higher) is necessary for distributed training across multiple GPUs or nodes. This ensures efficient data transfer between machines.
For cloud-based solutions, Tencent Cloud offers GPU-accelerated instances (like GN10X/GN7) with NVIDIA H100/A100 GPUs, high-performance CFS storage, and elastic networking to support DeepSeek model training and deployment efficiently.