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NVIDIA B300 GPU Server – Technical Specifications
The NVIDIA B300 GPU server is designed for demanding AI workloads, including large language model (LLM) training, generative AI, model fine-tuning, AI inference and high-performance computing (HPC).
Below is a technical specification format suitable for a Cyfuture AI product page. Exact specifications depend on the server configuration and whether it uses individual B300 GPUs or an NVIDIA HGX B300 platform.
Technical Specifications Table
Specification	NVIDIA HGX B300 GPU Server
GPU Architecture	NVIDIA Blackwell Ultra
GPU Configuration	8 × NVIDIA B300 SXM GPUs
GPU Memory	288 GB HBM3e per GPU
Total GPU Memory	Up to 2.3 TB
GPU Memory Bandwidth	Up to 8 TB/s per GPU
AI Performance	Up to 144 PFLOPS FP4 Tensor Core (sparse)
FP8 Performance	Up to 72 PFLOPS
GPU Interconnect	5th-generation NVIDIA NVLink
NVLink Bandwidth	14.4 TB/s aggregate
CPU	Server-specific Intel Xeon configuration
System RAM	Minimum 2 TB recommended for HGX B300 systems
Networking	NVIDIA ConnectX-8 SuperNICs, up to 800 Gbps per adapter
Storage	NVMe SSD storage; capacity depends on configuration
Expansion Interface	PCIe Gen 5
Virtualisation and Software	NVIDIA AI Enterprise and compatible AI frameworks
Primary Workloads	LLM training, fine-tuning, inference and HPC
Specifications are based on NVIDIA's HGX B300 platform documentation. Some performance figures use sparse computation, and actual server configurations may differ. 

NVIDIA HGX AI Factory
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Key Applications
- Generative AI: Train and deploy large language models and multimodal AI models.
- LLM inference: Serve high-volume AI workloads and reasoning models.
- Model fine-tuning: Adapt foundation models to enterprise datasets.
- AI research: Support deep learning, simulation and advanced data science.
- High-performance computing: Accelerate compute-intensive scientific and engineering workloads.