NVIDIA B300 Features and Capabilities

FAQs

• What is the NVIDIA DGX B300 and what is its foundational architecture?

  The NVIDIA DGX B300, launched in March 2025, is built upon the new Blackwell Ultra architecture. Marking a significant advancement in AI infrastructure, the B300 is designed to handle complex reasoning, real-time inference, and generative AI workloads simultaneously on a single platform, distinguishing it from older systems primarily intended for training.

• How does the B300 transform the AI workflow by supporting the full model lifecycle?

  The B300 is engineered to handle all stages of the AI lifecycle—including training, fine-tuning, and inference—on a single platform. This capability eliminates the need to split workloads across different machines, which typically slows down workflows and fragments data. By keeping everything in one place, the B300 ensures continuity, reduces delays, and allows AI models to move smoothly from experimentation straight into production, supporting the full AI lifecycle under one roof.

• What specific hardware features enable the B300 to efficiently handle long-context and reasoning-heavy workloads?

   To handle deep-chain attention, which is critical for models that reason or plan step-by-step, the B300’s architecture accelerates attention layers roughly 2×. In terms of memory, each GPU packs 288 GB of HBM3e, culminating in a system total of 2.3 TB across the system. This substantial memory capacity is crucial for feeding models with extremely long context windows, such as processing a million tokens, thereby ensuring high throughput for reasoning-heavy workloads without memory bottlenecks. 

• How does the NVIDIA DGX B300 ensure high-speed data flow both internally and across multi-node clusters?

  The B300 incorporates a dedicated data movement layer to prevent data stalls, ensuring continuous, high-speed flow. Internally, the eight Ultra GPUs are interconnected using fifth-generation NVLink, creating a unified high-speed fabric that provides 14.4 TB/s of aggregate bandwidth. Externally, for multi-node AI workloads, the system integrates ConnectX-8 SuperNICs, capable of speeds up to 800 Gb/s. This external connectivity allows multiple B300 systems to link into larger clusters without bottlenecks, enabling distributed reasoning workloads to operate as efficiently as if they were a single system. 

• How does the B300 maintain consistent AI performance by managing infrastructure and security duties?

  The B300 maintains predictable performance by splitting AI compute and infrastructure control into two separate worlds. A BlueField-3 DPU (Data Processing Unit) serves as the system’s operational brain, offloading critical infrastructure tasks like networking, storage, encryption, and real-time security enforcement. This separation prevents these tasks from consuming GPU cycles, ensuring the Ultra GPUs focus purely on model execution and never get dragged into infrastructure duties, maintaining consistent AI performance even under mixed or bursty loads. Additionally, system control is consolidated into a hardened management layer built around a DC-SCM module, providing a secure firmware boundary and centralized telemetry for better lifecycle management.

• What software layers turn the B300 hardware into a stable, production-ready AI factory?

  The B300 arrives with an operational backbone focused on scaling and stability, coordinated by three key software layers: Mission Control serves as the factory operating layer, managing the system like a shared factory floor by balancing interactive work, long training jobs, and inference tasks through Run:ai–driven orchestration and real-time infrastructure intelligence. NVIDIA AI Enterprise is the model runtime layer, providing a secure, validated environment with optimized foundation model execution, eliminating the dependency and configuration problems often found in production. Finally, Dynamo is the inference acceleration layer, which is open-source and built for scaled-up reasoning services, delivering real response-time and QPS gains through better model residency and pipelined GPU execution.

• Where can organizations access and receive guidance for deploying the NVIDIA B300?

  Organizations can access the B300 systems through the Uvation Marketplace, which offers a streamlined platform for evaluating, purchasing, and deploying tailored configurations for enterprise-grade AI pipelines. The marketplace provides deployment guidance, centralized availability, and access to ongoing support alignment. Teams interested in adoption can also schedule a free consultation to select the right system and plan their deployment strategy.