NVIDIA B300 Servers: Powering the Next Generation of AI Reasoning

FAQs

• What is the NVIDIA B300 platform, and why has it been developed?

  The NVIDIA B300 (Blackwell Ultra) platform represents the latest generation of AI infrastructure specifically designed to support reasoning-heavy AI workloads, such as multi-step agents and long-context assistants. Unlike traditional AI models where inference was considered lightweight and cheap, these new systems generate and evaluate intermediate reasoning tokens during a “thinking phase,” which can increase compute requirements by orders of magnitude. The B300 platform serves as a critical stepping stone to sustain high-throughput, low-latency reasoning across thousands of concurrent tasks.

• How does the hardware in B300 servers differ from previous AI infrastructure?

  The B300 silicon, known as Blackwell Ultra, is engineered for sustained model residency rather than bursty execution. It features a shift from 8-high to 12-high HBM3e stacks, providing 288GB of HBM per GPU and 2.3TB per 8-GPU node. This allows trillion-parameter models to remain fully resident in memory, avoiding performance bottlenecks. Additionally, it introduces a second-generation Transformer Engine supporting FP4 precision, which delivers approximately 1.5× higher inference throughput than the previous B200 models.

• What are the primary system architectures available for deploying B300 GPUs?

  B300 GPUs are delivered through three main architectures tailored to different operational needs:

  • NVIDIA DGX B300: A fully integrated, standardised platform in a 10U chassis, featuring front-facing I/O and isolated system management for simplified enterprise operations.

  • NVIDIA HGX B300: A reference design for OEMs (like GIGABYTE and ASRock Rack) that allows for custom CPU selection, cooling, and platform layouts.

  • NVIDIA GB300 NVL72: A rack-scale solution that treats the entire rack as a single logical system, connecting 72 Blackwell Ultra GPUs with liquid cooling to handle frontier-scale reasoning.

• What infrastructure challenges must be addressed when installing B300 servers?

  Deploying B300 systems requires a shift in data centre strategy because performance is now bounded by power delivery and thermal management. Each B300 GPU has a TDP of up to 1,100W, pushing rack power requirements into the 50kW to 120kW range. Because these sustained thermal loads often exceed the limits of traditional air cooling, liquid cooling is becoming the default. Furthermore, high-density compute must be supported by 800Gb/s InfiniBand XDR networking to prevent the interconnect from becoming a bottleneck during multi-node reasoning tasks.

• How does the B300 fit into NVIDIA’s future technology roadmap?

  The B300 is positioned as a deliberate bridge between the current Blackwell generation and the upcoming Rubin (R100) platform. While it maximises current HBM3e and interconnect capabilities, it establishes the operational patterns—such as liquid cooling and rack-scale thinking—that will be mandatory for future systems. Investing in B300 infrastructure allows organisations to scale reasoning workloads immediately while preparing their facilities for the next leap, which will include HBM4 memory and the Vera CPU.

• Where can organisations find and compare different B300 system configurations?

  The Uvation Marketplace serves as a neutral platform for discovering and evaluating B300-based infrastructure from various vendors. It enables teams to perform side-by-side comparisons of power, cooling, and networking requirements across DGX, HGX, and rack-scale configurations. This helps ensure that chosen designs align with real-world data centre constraints and provides access to deployment-ready systems rather than isolated components.