NVIDIA B100 192GB Blackwell Tensor Core GPU

Brand: NVIDIA | Category: GPUs

SKU: 900-21110-0100-000 | Part #: 900-21110-0100-000 | MPN: 900-21110-0100-000

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Memory capacity and bandwidth are the primary bottlenecks in large-model AI workloads, and the NVIDIA B100 192GB Blackwell Tensor Core GPU addresses this constraint directly. The B100 ships with 192GB of HBM3e memory, the fastest stacked DRAM technology available for GPU compute, delivering the memory density and throughput required to run inference on models that would otherwise demand distributed clusters. This is not a marginal upgrade: the combination of 192GB capacity and HBM3e bandwidth means a single GPU can hold and process models that previously required multiple devices, reducing latency, simplifying software orchestration, and lowering total cost of ownership for inference-heavy workloads.

The Blackwell architecture underlying the B100 represents a meaningful shift in how NVIDIA structures compute for AI. The part number 900-21110-0100-000 identifies this specific configuration, and it is built for environments where floating-point 8-bit (FP8) precision has become the practical standard for production inference. The B100 delivers up to 7 petaflops of FP8 performance, a figure that reflects not just raw clock speed but the efficiency gains from Blackwell's tensor operations redesign. For AI infrastructure teams evaluating whether to refresh their GPU inventory, this performance-per-watt profile matters: the 700W thermal design power is aggressive but justified by the computational density packed into the SXM form factor. Running multiple B100 units in a single server frame means fewer physical machines, simpler cooling strategies, and lower facility power overhead.

Deployment of the B100 requires serious planning, and that is by design. This is not a card for casual experimentation; it is the tool of choice for data-centre architects and AI ops teams who own the responsibility for provisioning inference clusters that serve real production workloads. The 700W power supply requirement per unit means rack-level power design must account for instantaneous draw across all populated slots. The SXM form factor ensures that NVIDIA's ecosystem of support—driver optimization, CUDA kernel libraries, TensorRT inference engine tuning—is fully leveraged. NVIDIA's software maturity around Blackwell-class devices means that teams moving to the B100 inherit not just silicon but years of optimization work in compilation, profiling, and deployment patterns that have already proven themselves at scale.

The B100's memory architecture deserves specific attention because it shapes the entire value proposition. HBM3e stacks memory directly on the GPU die, eliminating the latency penalty of off-package DRAM access. This matters profoundly for transformer-based models, where attention operations are memory-bound rather than compute-bound. The 192GB capacity means a single B100 can hold the full weights of large-scale language models, mixture-of-experts ensembles, and multimodal models without model sharding across devices. For teams running inference on models in the 70B to 200B parameter range, this is material. A cluster of eight B100 units can serve inference loads that would have required twenty or thirty older-generation cards, and the operational complexity of managing that smaller cluster is measurably lower. NVIDIA's positioning of the B100 reflects this reality: the device is priced and packaged for the inference-at-scale segment, not for research or development pipelines.

Omnixon Global supplies the NVIDIA B100 192GB Blackwell Tensor Core GPU (part number 900-21110-0100-000) to data centres, AI labs, and enterprise infrastructure teams across the GCC, South Asia, and Europe. We maintain regional inventory and provide the technical due diligence required for GPU procurement at scale: configuration validation, power budget review, cooling assessment, and driver support liaison. If your organization is planning a GPU refresh or expanding inference capacity, we invite you to request a formal quotation and technical consultation.

Performance and Memory Specifications

• 192GB HBM3e memory with dedicated high-bandwidth stacking for transformer inference and large-batch training
• Up to 7 petaflops of FP8 floating-point performance, optimized for quantized production models
• 700W thermal design power; enterprise-class 700W power supply required per unit
• SXM form factor compatible with NVIDIA's standard GPU mounting ecosystem and certified server platforms
• Blackwell architecture with tensor core improvements for improved throughput on matrix operations
• Full NVIDIA software stack support including CUDA 12, TensorRT, and cuDNN optimization libraries
• Part number 900-21110-0100-000 ensures accurate procurement and supply chain tracking