NVIDIA B300 The foundational building block for reasoning AI, raising the bar for real-time AI performance. Beyond.pl AI Panel Send an inquiry Built for the era of AI reasoning NVIDIA B300 is an AI powerhouse purpose-built to deliver hyperscale performance in an enterprise-sized footprint. As a fully integrated system, NVIDIA B300 delivers unprecedented performance and hyperscale-grade efficiency. By combining exceptional training performance with leadership-class, real-time inference, B300 empowers every organization to build scalable infrastructure for the era of AI reasoning. Beyond.pl makes this level of computing performance available within an enterprise-ready environment designed for demanding AI workloads. Whether supporting a single large-scale deployment or a long-term AI transformation program, our AI Factory combines enterprise-grade infrastructure with the expertise needed to develop, deploy and scale AI projects. Contact us to explore its capabilities and discuss how we can support your AI project. Graphics: Baseboard 8xB300 Source: NVIDIA The benefits of scaling AI infrastructure with Beyond.pl Faster access to AI capacity Launch AI projects without long infrastructure lead times. With 2 MW of GPU capacity available in Q1 2027, an additional 12 MW of high-density, liquid-cooled GPU capacity scheduled for H2 2027, and another 24 MW available in 2028, Beyond.pl provides a clear capacity roadmap for organizations planning to scale their AI infrastructure over the long term. Designed for large-scale Built for dedicated AI environments - not one-size-fits-all deployments. Beyond.pl supports large-scale AI projects with access to NVIDIA B300, GB300, GB200 or Vera Rubin platforms, enabling organizations to deploy the architecture that best matches their performance, operational, and growth requirements. AI infrastructure on your terms Your AI infrastructure should adapt to your business - not the other way around. We work closely with your team to deliver the right platform, capacity, and deployment model for your workloads, ensuring your AI environment evolves alongside your long-term strategy. Use cases for NVIDIA B300 Large language models at production scale models Develop, fine-tune, and deploy large language models on infrastructure designed for demanding AI workloads. With increased GPU memory and high-performance compute, NVIDIA GPUs enables larger models, extended context windows, and efficient inference at production scale. Multimodal AI applications Build AI solutions that process and combine multiple data types, including text, images, audio, and video. NVIDIA B300 provides the balanced compute performance and memory bandwidth required to train, optimize, and deploy advanced multimodal models. Reasoning AI and autonomous agents Support the next generation of reasoning models and AI agents with infrastructure built for complex inference workloads. NVIDIA B300 delivers the memory capacity and compute performance needed to sustain high-throughput token generation and scale advanced AI applications. NVIDIA B300 specification Download datasheet Form factor (DGX variant) 10 Rack Units GPU 8x NVIDIA Blackwell Ultra SXM GPU Memory | Bandwidth 2.1 TB HBM3e, 62 TB/s NVLink Bandwidth 14.4 TB/s Fast Memory 37 TB FP4 Tensor Core Performance 144 PFLOPS | 108 PFLOPS FP8/FP6 Tensor Core Performance 72 PFLOPS System Memory 2 TB, configurable to 4 TB CPU (DGX variant) Intel Xeon 6776P processors Networking (DGX variant) 8x OSFP ports serving 8x NVIDIA ConnectX-8 VPI >Up to 800 Gb/s of NVIDIA InfiniBand/Ethernet 2x dual-port QSFP112 NVIDIA BlueField®-3 DPU >Up to 400 Gb/s of NVIDIA InfiniBand/Ethernet Management Network (DGX variant) 1 GbE onboard network interface card (NIC) with RJ45 1 GbE RJ45 host baseboard management controller (BMC) Storage (DGX variant) OS: 2x 1.9 TB NVMe M.2 Internal storage: 8x 3.84 TB NVMe E1.S Source: NVIDIA datasheet The launch of the first commercial AI Factory in CEE In the second half of 2025, Beyond.pl launched the first commercial AI Factory in the CEE region in collaboration with NVIDIA. The platform was built on NVIDIA DGX SuperPOD reference architecture powered by NVIDIA B200 GPUs, providing enterprise-grade infrastructure for AI training and inference workloads Read the press release Explore more NVIDIA AI infrastructure for your next deployment NVIDIA GB300 Large-scale compute for the next generation of AI models and agentic systems. The NVIDIA GB300 NVL72 is a fully liquid-cooled, rack-scale system with 72 NVIDIA Blackwell Ultra GPUs and 36 NVIDIA Grace™ CPUs designed for highly complex AI environments that require powerful compute capacity, fast interconnects and efficient workload scaling. Explore NVIDIA GB200 Rack-scale AI infrastructure for large, distributed workloads. NVIDIA GB200 is built for organizations developing and operating complex AI environments at scale. It supports large model training, advanced inference and compute-intensive workloads that require tightly integrated GPU, networking and software resources. Explore NVIDIA B200 High-performance compute for training, fine-tuning and inference of advanced AI models. NVIDIA B200 is designed for organizations running demanding generative AI, large language model and high-performance computing workloads. It provides access to NVIDIA Blackwell architecture in an integrated enterprise AI system. Explore FAQ What is NVIDIA B300 The NVIDIA B300 is an 8-GPU server platform built on NVIDIA Blackwell Ultra architecture. Each GPU carries 270 GB of HBM3e memory and fifth-generation NVLink interconnects, designed for large-scale AI training and high-throughput inference workloads. What is the difference between DGX B300 and HGX B300? The core difference between these two Blackwell Ultra systems comes down to how the complete system is delivered and supported. DGX B300 is a fully integrated NVIDIA system, designed, validated and supported by NVIDIA. HGX B300, on the other hand, is an NVIDIA GPU baseboard platform with eight Blackwell Ultra GPUs interconnected via NVIDIA NVLink™, which OEM server manufacturers integrate into their own server designs. In short, DGX B300 is a complete NVIDIA system with NVIDIA hardware support, while HGX B300 provides the foundation for OEM-built servers, with system-level hardware support typically provided by the respective server manufacturer. What is the key difference between NVIDIA B300 and NVIDIA B200? The key difference is that B300 uses the newer Blackwell Ultra GPUs, optimized especially for large-scale reasoning and inference workloads, while B200 uses the original Blackwell B200 GPUs. B300 provides 1.5× higher dense FP4 performance and 2× higher attention performance than the B200. Another major improvement is memory capacity. B200 has 1,440 GB of GPU memory, whereas B300 provides roughly 2.1–2.3 TB, depending on the system configuration. That’s about 60% more HBM, which is particularly valuable for very large LLMs, long context, KV cache, and inference batching. What is the key difference between B300 and GB300? The key difference between B300 and GB300 is the server architecture. B300 is an x86-based server with Intel Xeon CPUs (DGX variant) and 8 NVIDIA B300 GPUs, while GB300 is based on the Grace Blackwell architecture, combining NVIDIA Grace CPUs with Blackwell Ultra GPUs. In short, B300 follows a traditional x86 CPU + GPU architecture, while GB300 uses NVIDIA’s tightly integrated Grace Blackwell CPU-GPU architecture. Is NVIDIA B300 available as a self-service offering? No. NVIDIA B300 is deployed as a tailored infrastructure solution. Because each deployment is designed around specific workload and performance requirements, our team will help you determine the right configuration, capacity, and deployment timeline. How do I get access to NVIDIA B300? Connect with Beyond.pl technical team to explore the best deployment strategy for your AI initiatives. We’ll help you assess workload demands, determine the right cluster size, and build a solution tailored to your requirements. Ready to run AI at scale? Deploy demanding AI workloads on NVIDIA GB300 NVL72 infrastructure at Beyond.pl AI Factory. Check available capacity and let us design a dedicated compute environment tailored to your AI workloads. Contact us
