1. GPU (Critical for AI Acceleration)

• Primary GPUs:

  • NVIDIA H100 Tensor Core GPU (80GB/94GB HBM3 VRAM) – Ideal for large-scale distributed training.

  • NVIDIA A100 80GB – A proven choice for high-performance LLM workloads.

  • Consumer Alternative: NVIDIA RTX 4090 (24GB GDDR6X) – For budget-conscious inference-only setups.

• Quantity: 4–8 GPUs (multi-GPU scaling via NVLink/Switch for parallel training).

• Interconnect: NVIDIA NVLink 4.0 or InfiniBand HDR (200Gbps+) for multi-GPU communication.

2. CPU

• Recommended:

  • AMD EPYC 9654 (96 cores, 192 threads) – Optimal for data preprocessing and GPU coordination.

  • Intel Xeon w9-3495X (56 cores) – High clock speeds for single-threaded tasks.

• Minimum: 32+ cores for handling multi-GPU workflows.

3. RAM

• Capacity: 512GB–1TB DDR5 ECC RAM (8+ channels for bandwidth).

• Speed: DDR5-4800+ MHz to avoid CPU bottlenecks.

4. Storage

• Primary Storage:

  • 2x NVMe Gen5 SSDs (e.g., Samsung 990 Pro 4TB) in RAID 0 for dataset caching (14GB/s+ read speeds).

• Secondary Storage:

  • 100TB+ NAS/SAN (e.g., Synology HD6500) with 25/100GbE for long-term data storage.

5. Motherboard & Power

• Motherboard: Server-grade board with PCIe 5.0 x16 slots (e.g., Supermicro AS-2125BT-HNMR).

• Power Supply: 1600W–2000W 80+ Titanium PSU (or redundant PSUs for servers).

6. Cooling

• Liquid Cooling: Custom loop or enterprise-grade AIO for GPUs/CPUs.

• Server Chassis: Rack-mounted (e.g., Dell PowerEdge C4140) with high airflow.

7. Networking

• Enterprise Switch: NVIDIA Quantum-2 InfiniBand or 100GbE Ethernet for multi-node clusters.

8. Software Stack

• OS: Ubuntu 22.04 LTS (optimized for CUDA).

• AI Frameworks: PyTorch 2.0+ with CUDA 12.x and cuDNN 8.9+.

• Orchestration: Kubernetes/Docker for distributed training.

Use Case Considerations

• Training: Multi-GPU setups (H100/A100) with NVLink are mandatory for efficiency.

• Inference: Single H100 or A100 (80GB) can handle most real-time LLM tasks.

• Budget-Friendly: Scale down to 2x RTX 4090 GPUs + 128GB RAM for small-model experiments.

For multi-node clusters, add high-speed interconnects (InfiniBand) and orchestration tools like SLURM or Ray.