Aiserver Manufacturer
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| System Architecture: |
* Chipset: GB10 Grace Blackwell Superchip * Combines a 20-core Arm CPU with a Blackwell GPU via NVLink-C2C interconnect * Coherent unified memory architecture for CPU-GPU data sharing |
| CPU: |
20-core Arm v9 processor * 10× Cortex-X925 cores (high-performance) * 10× Cortex-A725 cores (energy-efficient) |
| GPU: |
Blackwell architecture with: * 6,144 CUDA cores (equivalent to RTX 5070) * 5th-generation Tensor Cores (1,000 AI TOPS FP4 precision) * 4th-generation RT Cores (ray tracing acceleration) |
| Video Engines: | 1× NVENC (9th gen), 1× NVDEC (5th gen) |
| System Memory: |
28GB LPDDR5X unified memory * 256-bit interface, 273GB/s bandwidth * Supports models up to 200B parameters locally, 405B parameters with dual-node clustering |
| Storage: | 4TB NVMe M.2 SSD (self-encrypting) |
| Networking: |
* ConnectX-7 Smart NIC (200Gbps RDMA for clustering) * 10GbE RJ45 port * Wi-Fi 7 (802.11be) + Bluetooth 5.3 |
| Interfaces: |
* 4× USB4 Type-C (40Gbps) * 1× HDMI 2.1a (supports 8K video output) * Audio: HDMI multichannel audio |
| Power Consumption: |
* 170W typical operation * Up to 224W peak under heavy load |
| Cooling: | Passive airflow design optimized for silent operation |
| Physical Specifications: |
* Dimensions: 150mm (L) × 150mm (W) × 50.5mm (H) * Weight: 1.2kg * Form Factor: FHFL dual-slot (rack-mountable with optional kit) |
| Operating System: |
DGX OS (Ubuntu 22.04-based) * Pre-installed AI Enterprise software stack * Supports frameworks: PyTorch, TensorFlow, RAPIDS |
| Model Capabilities: |
* Inference: Up to 200B parameters (e.g., LLaMA 2, DeepSeek-R1) * Fine-tuning: Up to 70B parameters * Distributed training: Scale to 405B parameters with dual DGX Spark nodes |
| Security: |
* Secure Boot and ECC memory protection * Confidential computing with ARM TrustZone |
| Development Tools: |
* AI Enterprise Suite (cuDNN, TensorRT, etc.) * NGC container registry access |
Designed for 24x7 enterprise data center operations, it features enterprise-grade components and energy-efficient hardware optimized for deployment at scale.
