NVIDIA B300 Performance Explained: Scaling AI Reasoning and Inference

FAQs

• What is the NVIDIA B300 and how does it differ from previous GPU generations?

  The NVIDIA B300 is an inference-first GPU designed specifically for the current phase of AI adoption, where the priority has shifted from raw training speed to reasoning-heavy inference. Unlike general-purpose GPU refreshes, the B300 is built to handle the demands of modern large language models that must reason, plan, and execute multi-step tasks in real time. It focuses on delivering predictable latency at scale, high-concurrency inference, and improved performance for models with long context windows.

• What architectural advancements allow the B300 to achieve its reported 1.5× performance increase?

  The B300 achieves approximately 1.5× higher performance than standard Blackwell GPUs through a unified dual-die architecture, which integrates 208 billion transistors connected via a 10 TB/s NV-HBI interconnect. This design allows the GPU to behave as a single logical accelerator, reducing the latency and synchronization overhead typically found in multi-chip designs. Additionally, the introduction of NVFP4—a 4-bit floating-point format—nearly doubles effective compute density and reduces the model memory footprint by roughly 1.8× while maintaining accuracy for reasoning workloads.

• How does the B300's memory subsystem address the "memory wall" in large-scale AI?

  To prevent performance bottlenecks when models spill into slower CPU memory, the B300 features 288 GB of HBM3e memory, which is a 50% increase over the B200 and 3.6× more than the Hopper H100. This massive capacity allows 300B+ parameter models to remain fully resident on the GPU, supported by 8 TB/s of memory bandwidth to keep compute units consistently fed. Furthermore, a new on-chip structure called Tensor Memory (TMEM) enables up to 16 TB/s of internal read bandwidth, reducing register pressure and allowing for more efficient data reuse during complex matrix operations.

• Why is the B300 particularly effective for long-context reasoning and transformer models?

  The B300 introduces targeted enhancements to the Special Functions Unit (SFU), specifically to accelerate attention layers, which are often the most computationally expensive part of inference. These improvements result in 2× faster attention performance compared to standard Blackwell and 2.5× faster compared to Hopper. For agentic systems, this means a faster time-to-first-token, reduced latency variability for long-context prompts, and a lower overall compute cost per generated token.

• How does B300 performance scale from individual units to full AI factory deployments?

  Performance scales through two primary platforms: the HGX B300 and the GB300 NVL72. The HGX B300 is an eight-GPU node that functions as a single virtual accelerator with over 2 TB of unified memory. For even larger requirements, the GB300 NVL72 connects 72 GPUs within a single NVLink domain, offering a 50× increase in AI factory output performance compared to Hopper-based racks. This architecture ensures that as workloads grow, the infrastructure maintains high throughput and consistent utilization.

• How can organisations begin deploying NVIDIA B300 systems?

  Enterprises can access the NVIDIA B300 through the Uvation Marketplace, which offers configurations ranging from single HGX B300 nodes to full-scale GB300 NVL72 racks. Uvation provides architectural guidance and expert consultations to help organisations match specific B300 configurations to their inference workloads, ensuring optimized performance and a better cost per token for production-grade AI.