What Is the Price of NVIDIA B300 GPU?

The NVIDIA B300 GPU does not have one publicly fixed price because the final cost depends on the configuration, supplier, region, availability, cooling requirements, taxes, and support contract. For cloud access, current market listings indicate that NVIDIA B300 rentals generally begin at approximately US$7 per GPU hour on specialised GPU platforms and may exceed US$15 per GPU hour on hyperscale cloud services. Some providers offer lower rates through reserved or long-term commitments, while on-demand pricing is usually higher.

For purchasing a physical NVIDIA B300 GPU or an HGX B300 server, buyers generally need to request a custom quotation. The total investment may include the GPU, server platform, high-speed networking, liquid-cooling infrastructure, power delivery, storage, software, installation, and technical support. Therefore, cloud rental is often the more practical option for organisations that want to test B300 performance without making a large upfront investment.

What Is the NVIDIA B300?

The NVIDIA B300 is part of NVIDIA’s Blackwell Ultra platform and is designed for demanding artificial intelligence, machine learning, and high-performance computing workloads. It is built for large language model training, AI inference, reasoning models, synthetic data generation, scientific computing, and other workloads that require substantial memory and processing capacity.

The B300 features up to 288 GB of HBM3e memory, approximately 8 TB/s of memory bandwidth, and support for advanced FP4 and FP8 AI computation. These capabilities allow organisations to process larger models and datasets while reducing the need for frequent data movement between system memory and GPU memory.

The GPU also supports fifth-generation NVLink connectivity and is designed for high-density AI systems. Due to its high power consumption and thermal output, B300 servers typically require advanced cooling—particularly direct liquid cooling in multi-GPU configurations.

NVIDIA B300 Pricing Models

1. On-Demand Cloud Pricing

On-demand pricing allows users to rent B300 capacity by the hour without making a long-term commitment. Based on current market listings, rates may range from approximately US$7 to US$18 per GPU hour, depending on the provider, region, instance configuration, and service level.

This model is suitable for:

Short-term model testing.

Development and experimentation.

Temporary inference workloads.

Startups with unpredictable GPU requirements.

Teams that want to avoid hardware procurement.

The main advantage is flexibility. However, on-demand pricing can become expensive for continuous workloads.

2. Reserved or Committed Capacity

Reserved pricing is offered when a customer commits to using a B300 GPU for a specific period, such as one month, one year, or several years. The hourly equivalent may be lower than on-demand rates, but the customer usually agrees to a minimum usage commitment.

This model is useful for:

Regular AI model training.

Production inference.

Enterprise AI platforms.

Dedicated research workloads.

Businesses with predictable GPU demand.

3. Bare-Metal B300 Servers

A bare-metal server provides direct access to the physical GPU without virtualisation overhead. The final price depends on the number of GPUs, server configuration, CPU, system memory, local storage, network fabric, and support requirements.

A multi-GPU B300 server may also require:

Direct-to-chip liquid cooling.

High-capacity power distribution.

400G or 800G networking.

High-speed NVMe or parallel storage.

Advanced monitoring and orchestration.

Dedicated technical support.

For this reason, physical B300 server prices are typically provided through vendor quotations rather than a standard public price list.

What Affects the Final Price?

Several factors influence the total NVIDIA B300 cost:

Number of GPUs: An eight-GPU node costs substantially more than a single-GPU instance.

Billing model: On-demand access is generally more expensive than reserved capacity.

Region and availability: Pricing varies according to power, data center location, demand, and GPU supply.

Cooling: High-density B300 systems may require liquid-cooling infrastructure.

Networking: AI clusters may require InfiniBand or high-speed Ethernet fabric.

Storage: Training workloads need fast NVMe, parallel file systems, or object storage.

Support: Managed Kubernetes, MLOps, monitoring, security, and technical assistance increase the overall cost.

Taxes and data transfer: Cloud bills may include applicable taxes, egress charges, and storage fees.

Frequently Asked Questions

Is NVIDIA B300 available for hourly rental?

Yes. Several specialised GPU cloud providers list B300 capacity for on-demand rental, although availability and pricing can change frequently.

Is renting a B300 cheaper than buying one?

Renting is usually more economical for short-term projects, testing, and variable workloads. Buying may offer better long-term value when the GPU will operate at high utilisation for several years.

What workloads benefit most from B300 GPUs?

B300 GPUs are designed for large-scale model training, reasoning workloads, generative AI, high-throughput inference, scientific simulations, and other compute-intensive applications.

Does the NVIDIA B300 require liquid cooling?

High-density B300 systems commonly require direct liquid cooling or another advanced thermal-management solution. The exact requirement depends on the server design, GPU count, rack density, and workload profile.

How can I get an accurate B300 quotation?

Share your expected GPU count, workload, usage duration, region, storage requirements, networking needs, and preferred billing model with a cloud provider. The provider can then recommend an on-demand, reserved, bare-metal, or managed GPU configuration.

Conclusion

The price of an NVIDIA B300 GPU depends on whether you rent cloud capacity, reserve a dedicated instance, or purchase a complete physical server. As a general market reference, cloud access may start at around US$7 per GPU hour and rise above US$15 per GPU hour for premium or hyperscale offerings. Hardware purchase costs require a customised quotation because cooling, networking, storage, power, and support can significantly affect the final investment.