NVIDIA DGX A100 Deep Learning System (8x A100, 640GB)

The DGX A100 occupies a specific place in AI infrastructure history: it is the machine that defined what an 8-GPU training node should look like. Every HGX A100 server on the market, including the Supermicro platforms in MillionMiner's own catalog, is built around the GPU baseboard NVIDIA designed for this system. Buying the DGX means buying the original, with everything NVIDIA layers on top of the silicon. The compute architecture. Eight NVIDIA A100 80GB SXM4 GPUs mount to the HGX baseboard and interconnect through six NVSwitches in a full-mesh topology. Each GPU carries 12 third-generation NVLinks, delivering 600 GB/s of bidirectional GPU-to-GPU bandwidth, with 4.8 TB/s of aggregate bisection bandwidth across the fabric. In practical terms, gradient synchronization across all eight GPUs completes fast enough that distributed training scales close to linearly, which is the entire reason SXM-plus-NVSwitch systems exist. The node totals 640GB of pooled HBM2e GPU memory and 5 petaFLOPS of AI performance, with INT8 inference throughput reaching 10 petaOPS. Multi-Instance GPU partitions each A100 into as many as seven isolated instances, so one DGX presents up to 56 hardware-partitioned GPUs for multi-tenant inference, a configuration that lets the same machine train by night and serve dozens of isolated workloads by day. The host platform is sized so the GPUs never wait. Dual 64-core AMD EPYC 7742 processors provide 128 cores and 256 threads for data loading, preprocessing, and augmentation. System memory reaches 2TB of DDR4. Storage splits between dedicated M.2 NVMe for the OS and 30TB of internal Gen4 NVMe for datasets, keeping training I/O off the boot path. Ten Mellanox ConnectX-6 interfaces include eight single-port 200Gb/s HDR InfiniBand adapters arranged one per GPU for GPUDirect RDMA, the topology that lets multiple DGX nodes exchange gradients directly between GPU memories across the network with no CPU in the path. The software stack, which is the half of the product white-box servers do not include. DGX OS, an Ubuntu-based distribution tuned for the hardware, ships installed. NVIDIA Base Command provides orchestration, scheduling, and fleet management. The NGC catalog supplies certified containers for every major training and inference framework, performance-validated against this exact system, so the gap between unboxing and first training run is measured in hours. NVIDIA AI Enterprise support covers the full stack under one contract: when a job fails, there is no integrator pointing at NVIDIA and NVIDIA pointing back. The scaling path is part of what you buy. The DGX A100 is the building block of NVIDIA's DGX SuperPOD reference architecture, the published blueprint for scaling from one node to clusters of twenty, one hundred, or more, with validated network topology, storage architecture, and management tooling. Organizations that start with one DGX inherit a documented road from single-node fine-tuning to cluster-scale training, rather than designing that road themselves. The build-versus-buy decision, stated honestly. MillionMiner sells both sides of it. The Supermicro AS-4124GO-NART+ in this catalog delivers the same eight-A100 NVSwitch GPU complex on a configurable host at a lower platform cost, and for budget-led teams comfortable owning their own integration and software stack, it is the rational pick. The DGX A100 is for the opposite buyer: enterprises and research institutions where procurement favors single-vendor accountability, where compliance requires a supported reference platform, and where the cost of engineers debugging integration issues exceeds the appliance premium. Time to first training run, one throat to choke, and a validated scaling path are the product. The DGX A100 remains the most widely deployed AI appliance ever built, with a software ecosystem that NVIDIA continues to maintain across current CUDA releases. As a 6U rackmount system drawing several kilowatts at full load, it belongs in a data center environment; MillionMiner confirms power and cooling requirements during configuration, ships worldwide DDP with customs handled, and offers hosting in its own facilities for teams that prefer not to provision infrastructure on-site.