Foundational AI Infrastructure for Demanding Workloads
NVIDIA GPUs housed in Tier 3+ data centers for unparalleled performance, reliability, and security.
The architecture high-performing teams trust
Hopper
HGX H200
Large-model memory
141GB of HBM3e memory and 4.8 TB/s of bandwidth for memory-heavy training and inference. Up to 1.9× faster inference than H100.
Blackwell
HGX B200
Faster training at scale
A second-generation Transformer Engine with FP8 and new precisions for efficient multi-node scaling. Up to 3× faster training than H100.
Grace Blackwell
HGX GB200
Real-time inference for LLM
Grace CPUs and Blackwell GPUs connected as one rack-scale system for trillion-parameter models. Up to 30× faster real-time inference than H100.
Blackwell Ultra
HGX B300
Next-gen training and inference
Blackwell Ultra GPUs, ConnectX-8 networking, and NVIDIA Mission Control software in a single platform. Up to 11× faster inference and 4× faster training than Hopper.
Grace Blackwell Ultra
HGX GB300
Reasoning at rack scale
Grace Blackwell Ultra with Quantum-X800 InfiniBand or Spectrum-X Ethernet and ConnectX-8 SuperNICs. Up to 50× higher AI factory output than Hopper.
Capacity and tooling
built for AI teams
Choose how you access compute, then add the orchestration, storage,
and observability your workloads need.
On-demand and reserved capacity
Choose reserved clusters or on-demand GPUs to fit every AI team, from startups to large-scale labs. Pay only for what you use, or reserve capacity and scale as you grow.
Managed Kubernetes
Secure clusters built on upstream Kubernetes, ready in minutes, with simplified multi-node orchestration for training, fine-tuning, and inference. Works with Kubernetes-native tools, GitOps, and Terraform.
AI-optimized storage
High-performance compute connected to a unified, VAST-powered storage layer built for AI. From checkpointing trillion-parameter models to sharing datasets across thousands of nodes, your data keeps pace with your workloads.
Real-time observability
Monitor the metrics that matter across your models, GPU usage, and inference pipelines. Spot bottlenecks before they impact workloads and keep AI costs under control.
Our customers
Voltage Park has consistently provided us with some of the most affordable and reliable access to H100 multi-node machines.
Voltage Park’s owned hardware and technology meets our critical needs.
I’m very, very happy with Voltage Park. I think it’s been very critical to our success over the past couple of months.
I didn’t want to tell people what cloud provider we’re using, because… the pricing is too good…
FAQs
Frequently asked questions comparing the B200 and B300
What’s the difference between the NVIDIA B200 and B300 in terms of performance and specifications?
The B300 offers twice the memory of the B200 (384 GB HBM3e versus 192 GB HBM3e) and double the memory bandwidth (~16 TB/s versus ~8 TB/s). The B300 is better equipped for high-density training (ex: LLMs, diffusion models) compared to the B200.
Which workloads are best suited for the B200?
Depending on TCO and memory need, the B200 is suited for model inference at scale, scientific computing, FP64 workloads, and multi-GPU systems (4 to 8 GPUs)
Which workloads are best suited for the B300?
The B300 is best suited for LLM training with its higher throughput and NVLink fabric, model inference at scale (depending on TCO and memory requirements), and supercomputers.
What is the difference in pricing and availability for the B200 and B300?
Both the B200 and B300 are currently available with a long-term contract at Voltage Park. Contact us for details.
Get the right GPU
for your workload
Dedicated performance, enterprise-grade reliability, reserved or on-demand.