NVIDIA HGX B300: Specs, Pricing and How to Reserve Your GPU VM

If you’re planning to train larger AI models, scale distributed workloads or deploy high-performance inference in production, choosing the right GPU becomes important. You need more compute, memory capacity and predictable performance. The NVIDIA HGX B300 is built for exactly that. In this guide, you’ll learn what it offers, when to choose it, its pricing and how to reserve it on Hyperstack.

What is NVIDIA HGX B300

NVIDIA HGX B300 is a next-generation GPU built to power generative AI, large-scale model training and production-grade inference workloads. The B300 is ideal for organisations that want s to train, fine-tune and deploy advanced models at scale. With focus on performance, scalability and reliability, the NVIDIA HGX B300 provides the foundation required to support demanding enterprise and research AI environments.

What are the Key Features of NVIDIA HGX B300

The NVIDIA HGX B300 gives you production-grade performance with its features:

Blackwell Ultra Architecture Performance

Built on the Blackwell Ultra architecture, the B300 delivers up to 144 PFLOPS of FP4 Tensor Core performance and 72 PFLOPS FP8/FP6 compute power. This means you can train and fine-tune LLMs faster, experiment with advanced generative AI and handle dense compute workloads without compromising throughput.

Massive 2.3 TB GPU Memory Capacity

With eight Blackwell Ultra GPUs providing a combined 2.3 TB of HBM3e memory and 64 TB/s aggregate bandwidth, you can keep extremely large models fully in memory. This allows you to reduce offloading, avoid memory bottlenecks and process massive datasets. You can run multimodal models, manage large parameter counts and accelerate checkpointing without constantly worrying about memory constraints.

NVLink Switch System for High-Speed Scaling

The dual NVIDIA NVLink Switch System delivers 14.4 TB/s of aggregate interconnect bandwidth across all eight GPUs. This enables near-linear scaling and efficient communication between GPUs so that you can run distributed training workloads with reduced latency. If you're using frameworks like DeepSpeed or Megatron-LM, you benefit from faster and improved scaling efficiency across your compute cluster.

Hyperscale AI Training Efficiency

With high-density multi-GPU compute and ultra-fast interconnect bandwidth, the NVIDIA HGX B300 is built for hyperscale AI training. You can train frontier-scale models, improve model throughput and shorten training cycles. This also helps you optimise cost per training run while maintaining consistent performance across long-running jobs.

Production-Ready AI Inference

NVIDIA HGX B300 supports up to 144 PFLOPS of sparse FP4 performance, making it ideal for real-time inference and advanced reasoning tasks. You can deploy high-throughput AI applications that require low latency and consistent responsiveness. No matter if you're serving LLM-based applications or running production inference pipelines, you can scale confidently without sacrificing speed or reliability.

When to Choose NVIDIA HGX B300 Over Other GPUs

High-end GPUs like NVIDIA A100 and NVIDIA H100 are ideal choices for a wide range of AI workloads. They offer strong performance per dollar and are highly effective for mid-sized model training, fine-tuning and production inference. For many teams, these GPUs are more than sufficient.

However, when you move into training frontier-scale models with extremely large parameter counts or running advanced multimodal systems at hyperscale, some GPUs can start to show limitations. Memory constraints, cross-instance communication overhead and scaling inefficiencies can slow down your experimentation and increase overall training time.

The NVIDIA HGX B300 is ideal when your workloads demand massive in-memory capacity, multi-GPU performance and near-linear scaling within a single system. Instead of stitching together multiple smaller GPU VMs, you operate in a high-density environment purpose-built for large-scale AI operations.

You should consider the NVIDIA HGX B300 if you:

• Are training models that exceed standard GPU memory limits
• Require consistent scaling across eight tightly connected GPUs
• Need maximum throughput for long-running AI training jobs
• Are deploying advanced reasoning systems in production
• Want infrastructure that supports next-generation AI growth

NVIDIA HGX B300 Pricing

The NVIDIA HGX B300 is available exclusively through reservation to ensure guaranteed access and capacity planning for large-scale AI projects. On Hyperstrack, the price starts from $3.50/hour

Reservation is ideal because:

• There is guaranteed availability during peak hours or urgent deployment cycles. You do not have to worry about NVIDIA HGX B300 going unavailable at the time you need it, for example, time-sensitive model training.
• You can stay in control with the Contract Usage tab. This is available only when you reserve, so you can track real-time GPU usage, estimate future consumption and prevent idle waste.
• You get priority engineer-led support for faster response times and escalated troubleshooting for critical workloads.

Steps to Reserve NVIDIA HGX B300 on Hyperstack

Due to high demand and enterprise-grade deployment requirements, the NVIDIA HGX B300 GPUs are available through a structured reservation process.

1. Visit the NVIDIA HGX B300 Reservation Page.
2. Complete the reservation form, including:
   
• Company name
• Use case (LLM training, generative AI, inference, etc.)
• Number of GPUs required
• Duration of reservation
  
3. Submit your request
4. The Hyperstack team will contact you to finalise deployment details and align infrastructure with your workload requirements

Ready to Get Started?

Here are some helpful resources that will help you deploy your NVIDIA HGX B300 on Hyperstack:

• New to Hyperstack? Sign up Today to Get Started

• Check out the Hyperstack API Documentation

• Explore the Quick Platform Tour

• Need help? Contact us at support@hyperstack.cloud

FAQs

What is NVIDIA HGX B300?

NVIDIA HGX B300 is an AI compute platform built on the Blackwell Ultra architecture, designed for hyperscale AI training, generative AI and real-time inference workloads.

How much memory does NVIDIA HGX B300 provide?

NVIDIA HGX B300 delivers 2.3 TB of combined HBM3e GPU memory across eight GPUs with 64 TB/s aggregate bandwidth.

Is NVIDIA HGX B300 suitable for LLM training?

Yes, NVIDIA HGX B300 is optimised for LLM training, distributed AI workloads and hyperscale model development.

How much does NVIDIA HGX B300 cost on Hyperstack?

The reservation pricing for the NVIDIA HGX B300 starts from $3.50 per hour on Hyperstack.

Can I reserve NVIDIA HGX B300 in advance?

Yes, NVIDIA HGX B300 is available exclusively through reservation to ensure guaranteed access and infrastructure planning.