jensonhwang – SockoPower

The NVIDIA DGX-1 was a purpose-built system for deep learning and AI research, released in 2016 (Pascal-based) and later updated (Volta-based).¹ It was essentially the world’s first “deep learning supercomputer in a box.”²

1. NVIDIA DGX-1 Key Specifications

The DGX-1 came in two main variants based on the GPU architecture: the initial Pascal (Tesla P100) version and the later, more powerful Volta (Tesla V100) version.³

Feature	DGX-1 (Pascal – Tesla P100)	DGX-1 (Volta – Tesla V100)
GPUs	8x NVIDIA Tesla P100	8x NVIDIA Tesla V100
Total Peak Performance (FP16)	170 teraFLOPS	1 petaFLOPS (1,000 teraFLOPS)
Total GPU Memory (HBM2)	128 GB (16 GB per GPU)	128 GB or 256 GB (16 GB or 32 GB per GPU)
GPU Interconnect	NVIDIA NVLink (hybrid cube-mesh network)	NVIDIA NVLink (300 GB/s inter-GPU bandwidth)
CPU	Dual 20-Core Intel Xeon E5-2698 v4 2.2 GHz	Dual 20-Core Intel Xeon E5-2698 v4 2.2 GHz
System Memory (RAM)	512 GB DDR4 LRDIMM	512 GB DDR4 LRDIMM
Storage	4x 1.92 TB SSD RAID 0	4x 1.92 TB SSD RAID 0
Network	Dual 10 GbE, 4 IB EDR	Dual 10 GbE, 4 IB EDR
Form Factor	3U Rackmount Chassis	3U Rackmount Chassis
Software	Pre-integrated Deep Learning Software Stack (CUDA, cuDNN, major frameworks, NVIDIA DIGITS, NVIDIA Docker)	Same pre-integrated stack, optimized for V100 Tensor Cores

2. Business Prospectus and Target Market

The DGX-1’s business strategy was to provide a turnkey, high-performance platform specifically optimized for the demanding computational needs of Deep Learning (DL) and Artificial Intelligence (AI) training, shifting the focus from custom server building to immediate productivity.

Core Value Proposition

The DGX-1 was marketed as the fastest path to deep learning, offering:

Revolutionary Performance: Delivering the computational power of many racks of conventional servers in a single box, dramatically accelerating model training time (up to 96X faster in some benchmarks compared to CPU-only servers).⁴
Effortless Deployment: It was a fully integrated system with hardware, deep learning software, and development tools pre-installed and optimized. This “plug-and-play” simplicity was a significant selling point, saving data scientists months of integration and configuration effort.
End-to-End AI Solution: It included the NVIDIA Deep Learning Software Stack (frameworks, libraries like cuDNN and NCCL, and tools like NVIDIA Docker), ensuring the hardware was utilized to its maximum potential.⁵
Enterprise Support: NVIDIA offered an enterprise-grade support model (DGXperts) to help customers maximize productivity and resolve critical issues, appealing to large companies and research institutions.⁶

Target Market

The primary customers for the DGX-1 were organizations leading the charge in AI and deep learning:

AI and Data Science Research Institutions: Universities and government labs requiring immense compute power for cutting-edge research.⁷
Enterprise AI Development: Fortune 1000 companies across various sectors (tech, automotive, healthcare, finance, consumer internet) that were building, training, and deploying their own production-grade AI models.
Cloud Service Providers (CSPs): Companies offering GPU-accelerated cloud instances for AI workloads.
High-Performance Computing (HPC): Organizations needing fast computation for accelerated analytics, scientific visualization, and large-scale simulation.⁸

In essence, the DGX-1 established NVIDIA’s brand as the leader in providing AI Infrastructure for the Enterpr