NVIDIA B300 and Generative AI

FAQs

• Why has running generative AI models reliably and at scale become the primary challenge for enterprises?

  The emphasis in generative AI has shifted, as building the models is no longer the hardest part; rather, the real difficulty lies in running them continuously, reliably, and at scale. Large language models (LLMs) are now expected to operate as autonomous agents, coordinate tools, retain long conversational context, and reason in production environments. This new phase means that the majority of compute is consumed during inference, which has exposed limitations in traditional GPU infrastructure, specifically regarding memory capacity, attention throughput, cost predictability, and power efficiency. 

• What is the NVIDIA B300, and how does it fit into the emerging AI Factory model?

  The industry is responding to these constraints by moving toward the AI Factory model, which is purpose-built infrastructure designed to support the full lifecycle of generative AI, treating inference as the primary workload rather than an afterthought. The NVIDIA B300, utilizing the Blackwell Ultra architecture, is a direct answer to this transition. It is engineered specifically for generative AI reasoning and inference, prioritizing the efficiency, memory scale, and throughput necessary to ensure the real-world viability of large models in production. 

• How does the B300 address memory limitations, which are often cited as the defining bottleneck for scaling generative AI models?

  As generative AI models scale, memory capacity and bandwidth have emerged as the defining constraints, often proving more limiting than raw compute. The B300 tackles this by integrating 288 GB of HBM3e memory per GPU, which represents a 3.6× increase over the 80 GB capacity available in the H100 generation. This capacity enables support for multi-trillion-parameter models, larger Mixture-of-Experts (MoE) configurations, and the extended context windows required by reasoning agents, all without excessive model partitioning. Furthermore, the B300 delivers up to 8 TB/s of HBM bandwidth per GPU, a 2.4× increase over the H100, ensuring that compute units are constantly fed with data to maintain sustained inference performance. 

• What is NVFP4 inference, and how does this technology impact the economics of deploying large models in production?

  The NVIDIA B300 introduces native NVFP4 inference, an innovation that fundamentally changes the economics of large-scale deployment by prioritizing ultra-low precision without sacrificing accuracy. NVFP4 is a 4-bit floating-point format implemented directly in the Blackwell Ultra hardware and is specifically tuned for transformer-based generative models. This technology delivers up to 4× higher inference performance compared to FP8, achieves 25–50× gains in energy efficiency, and offers a 3.5× reduction in memory footprint compared to FP16. To maintain the accuracy required for real-world workloads, the B300 employs a dual-level scaling mechanism that minimizes quantization error. 

• How does the NVIDIA B300 optimize performance for attention-heavy workloads and complex AI reasoning tasks?

  Performance in modern generative AI is increasingly defined by attention layers, particularly in tasks involving long-context reasoning, agentic workflows, and tool-using models. The B300 addresses this through the second-generation Transformer Engine, which integrates custom Blackwell Tensor Cores and is optimized for low-precision computation. Crucially, the B300 provides 2× faster attention-layer performance compared to earlier Blackwell GPUs. These architectural and software optimizations combine to yield 11–15× higher LLM throughput per GPU compared to the Hopper generation, making it highly effective for production inference where high throughput and responsiveness are essential. 

• How can enterprises access and evaluate the NVIDIA B300 infrastructure for their AI factory deployment?

  Enterprises can access NVIDIA B300–based platforms through the Uvation Marketplace, which provides a streamlined path for adoption. The marketplace allows organizations to access systems designed for generative AI training and inference, evaluate configurations aligned to memory-intensive or FP4-optimized use cases, and compare deployment options across various environments. Additionally, enterprises can engage with infrastructure experts via the marketplace to ensure that their system configurations are right-sized for both current and future scaling needs.