1. Basic Configuration

• Model: AIForce AIC-500
• Chassis: 4U Rackmount (482×900×175mm)
• Weight: 38kg (base), ≤55kg (max)

2. Core Hardware

• CPU:

• Model: AMD EPYC 7452 (32C/64T, 2.35-3.35GHz)
• Cache: 128MB L3
• TDP: 180W
• PCIe Lanes: 128×PCIe 4.0

• Memory:

• Capacity: 512GB (16×32GB)
• Type: DDR4-3200 8-channel RDIMM
• Max Expansion: 32 slots, up to 4TB

• GPUs:

• Model: 4×NVIDIA A100 80GB SXM4
• CUDA Cores: 6,912 per GPU
• VRAM: 80GB HBM2e (2TB/s bandwidth)
• Interconnect: NVLink 3.0 (900GB/s)

• Storage:

• Cache Tier: 2×3.2TB Intel Optane P5800X (6M IOPS random read)
• Data Tier: 8×7.68TB Solidigm D5-P5430 QLC (6.8GB/s read)

3. Networking

• Interconnect:
• 8×200Gb/s NVIDIA Quantum-2 InfiniBand
• Latency: <0.5μs (GPUDirect RDMA)
• Management:
• Dual 100GbE OCP NICs (with BMC)

4. Cooling & Power

• Cooling:
• Direct Liquid Cooling (DLC) modules
• Redundant fans (N+2)
• Power Supply:
• 2400W 2+2 redundant Titanium (96% efficiency)
• Input: 200-240V 3-phase AC

5. Performance

• AI Training:
• ResNet-50: 2,300 images/sec (FP32)
• BERT-Large: 1.2 samples/sec (FP16)
• GPT-3 175B: Scalable to 8-node training
• Certifications:
  ✓ NVIDIA DGX SuperPOD compatible
  ✓ MLPerf Training v2.1 benchmark suite

6. Software Stack

• Pre-installed:
• NGC Containers: PyTorch 2.1/TensorFlow 2.12
• Cluster Management: Kubernetes + Kubeflow
• Optimizations:
  ✓ Automatic Mixed Precision (AMP)
  ✓ Gradient Compression (1/8 sparsity)

7. Use Cases

• Primary:
  ✓ LLM Training (LLaMA/GPT)
  ✓ 3D Medical Imaging
  ✓ Autonomous Driving Perception
• Deployments:
  ▶ National AI Lab (16-node cluster)
  ▶ Top3 Cloud AIaaS Platform

8. Service

• Warranty: 5-year premium support (incl. liquid cooling)
• Professional Services:
  ✓ Model Parallelism Optimization
  ✓ Distributed Training Troubleshooting