The 2016 Arrival of the DGX-1
NVIDIA released the DGX-1 in 2016 to meet the computing demands of artificial intelligence. This system contains eight Tesla P100 GPUs based on the Pascal architecture. The system produces the throughput of 250 x86 servers in a single 3U box. It includes 16nm FinFET fabrication, NVLink for high-speed interconnect, HBM2 for big data workloads, and other breakthroughs. The system contains 170 teraflops of peak half-precision performance and 768 GB/s of bandwidth. The DGX-1 provides 7TB of SSD DL Cache and quad InfiniBand 100Gb networking. The software includes the NVIDIA Deep Learning GPU Training System (DIGITS), CUDA Deep Neural Network library (cuDNN) version 5, Caffe, and Torch. The DGX-1 delivers 12x faster training than Maxwell architecture solutions from the previous year. The NVIDIA DGX-1 delivers the equivalent performance of 250 x86 servers in a single 3U box, and it provides researchers with a tool that completes training tasks in two hours that would take a dual-socket Xeon E5-2697v3 server 150 hours to finish. Researchers at OpenAI used the first production DGX-1 to work on artificial intelligence problems. Jen-Hsun Huang, CEO of NVIDIA, said the DGX-1 was created to access superhuman capabilities and apply them to problems that were once unsolvable. Yann LeCun, director of AI Research at Facebook, said that Pascal delivers faster GPU-to-GPU communication and hardware that uses reduced-precision arithmetic. Xuedong Huang, chief speech scientist at Microsoft Research, said the Tesla P100 horsepower would enable Microsoft’s CNTK to accelerate AI breakthroughs.
Evolution of GPU Architectures
The progression of GPU architectures defines the ability to train larger neural networks. The industry moved from Pascal to Volta in 2017, Ampere in 2020, and Hopper in 2022. The H100 GPU contains 80 billion transistors and 16,896 CUDA cores. This chip delivers 990 TFLOPS of FP16 performance and has 80GB of HBM3 memory. The H100 utilizes the Transformer Engine and fourth-generation Tensor Cores. The Blackwell architecture succeeds Hopper. The B200 flagship chip uses 208 billion transistors and delivers more performance than the H100. The B200 has 192GB of HBM3e memory and 8.0 TB/s of memory bandwidth. The A100, which launched in 2020, has 80GB of HBM2e memory and 3.2 TB/s of bandwidth. The V100 released in 2017 has 32GB of HBM2 memory and 0.9 TB/s of bandwidth. The P100 launched in 2016 and provides 16GB of HBM2 memory.
| Model | Architecture | VRAM (GB) | FP16 (TFLOPS) |
|---|---|---|---|
| P100 | Pascal | 16 | 21.2 |
| V100 | Volta | 32 | 125 |
| A100 | Ampere | 80 | 312 |
| H100 | Hopper | 80 | 990 |
| B200 | Blackwell | 192 | – |
At the 2018 GPU Technology Conference, NVIDIA unveiled the DGX-2. This system provides two petaflops of performance to researchers. This performance is 1,000 times greater than a MacBook Pro. The V100 GPU was also released to provide the performance of up to 100 CPUs in one processor.
The AI Cluster Economy
AI training costs determine which hardware providers companies use. Specialized providers like GMI Cloud and Bitdeer focus on high-performance compute. GMI Cloud sells H100 GPUs starting at $2.00 per hour. In contrast, hyperscalers like AWS and Azure charge more for their integrated services. Bitdeer reported 4,184 deployed GPUs in April 2026 and maintained a 92% utilization rate. Two units of the DGX Spark line can connect via QSFP cable to pool memory to 128GB. This configuration supports up to 200-billion-parameter models. You know that training efficiency determines whether a startup survives its first two years. The total cost involves hourly rates and hidden fees for data egress or storage. Companies like Lambda and Runpod also sell H100 and B200 instances by the hour. Runpod lists B300 at $7.39 per hour and H200 at $4.39 per hour. The AMD Instinct MI300A has 128GB of VRAM, which is 60% more than the H100. The xAI project plans a 100,000 GPU cluster to power its companies. The GB200 NVL72 delivers 30x faster large language model inference than the same number of H100 GPUs. This system also uses 25x less energy for certain workloads. Companies like Meta and Microsoft compete to build intelligent AI. The B200 is expected to be priced between $30,000 and $40,000, which is higher than the H100’s current cost of $25,000. Will the massive investment in Blackwell architecture satisfy the demand for even larger models?
