Blackwell Ultra rackLive Marketplace Asks Available

NVIDIA GB300 NVL72

GB300 is the NVL72 rack built from B300 GPUs and Grace CPUs. A full rack is 72 B300 GPUs and 36 Grace CPUs on one NVLink 5 domain, with about 20 TB of HBM3e. Smaller slices still sit in that architecture. It is the rack you ask for when a single 8-GPU node is not enough memory.

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Flexible Compute Modes

Deploy NVIDIA GB300 NVL72 Your Way: Docker Pods or Root KVM VPS

NovaPod lets you decide how you want to harness GPU power. Whether you need an instant pre-cached container stack, a full root virtual machine with dedicated public IP, or a multi-node NVLink cluster, pay only for what you run with second-by-second crypto billing.

Docker GPU PodCold Boot < 15s

Fast, Lightweight AI Containers

Pure GPU compute without hypervisor overhead. Boot straight into PyTorch, vLLM, ComfyUI, or JupyterLab.

1-second precision metering
37+ Pre-cached ML framework templates
Zero idle fee when instance is stopped

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Root KVM VPSPersistent NVMe

Full Root Virtual Machine

Dedicated operating system (Ubuntu 24.04 LTS / Windows GPU Server) with complete sudo root SSH access from minute one.

Dedicated public IPv4 (all ports open)
Pause anytime; pay only $0.00014/GB/hr for disk
Run Docker-in-Docker, k3s, custom drivers

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Multi-GPU Cluster900 GB/s NVLink

Supercomputing & Distributed Training

Pool up to 8× NVIDIA GB300 NVL72 on a unified node fabric with Quantum-2 InfiniBand RDMA for multi-node scale.

Zero-loss all-reduce gradient exchange
PyTorch FSDP, DeepSpeed Zero-3 ready
Native GPUDirect Storage & RDMA fabric

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Hardware Specifications

What it is: NVL72 rack, not one GPU
GPUs: 72 × B300
CPUs: 36 × Grace, 72-core Neoverse V2
GPU memory: 288 GB HBM3e each
Rack HBM: About 20 TB
Host memory: About 17 TB LPDDR5X
FP4 sparse: About 1.44 EFLOPS
FP4 dense: About 1.08 EFLOPS
NVLink 5: About 130 TB/s in the rack
GPU to CPU: 900 GB/s NVLink-C2C
Networking: ConnectX-8, 800 Gb/s
Cooling: Liquid
Smallest slice: 8 GPUs
Larger steps: 36 GPUs, then 72

Rack, not a single GPU

GB300 is the NVL72 system: 72 B300 GPUs and 36 Grace CPUs on one NVLink 5 domain. Each GPU is a Blackwell Ultra part with 288 GB of HBM3e. The rack total is about 20 TB of HBM3e, plus on the order of 17 TB of LPDDR5X on the Grace CPUs. Sparse FP4 for the rack is quoted near 1.44 EFLOPS and dense FP4 near 1.08 EFLOPS.

NVLink 5 inside the rack is about 130 TB/s. GPU-to-CPU traffic uses NVLink-C2C at about 900 GB/s, which is the point of Grace: tool calls, retrieval, and orchestration do not cross a slow PCIe hop on every step. Networking on the reference design is ConnectX-8 at 800 Gb/s. The rack is liquid cooled.

How a rental is sliced

You do not have to take all 72 GPUs. The practical minimum is one 8-GPU node. From there the steps are a half rack (36) and a full rack (72), then more than one rack if the run needs it. A smaller slice still lives in the NVL72 fabric, so you keep the same interconnect at a lower GPU count.

A lone B300 on an x86 host is a different product. Same silicon, no Grace domain, no 20 TB of unified HBM. Use that when the model fits in one node and you want per-second billing without a rack conversation.

Who it is for

The clear full-rack jobs are models that do not fit in the roughly 2.3 TB of an 8× B300 node: trillion-parameter dense and mixture-of-experts training, frontier inference at high concurrency, and long-context serving with a large KV cache. Smaller GB300 allocations still make sense when you specifically want Blackwell Ultra and Grace, not when any 8-GPU Hopper box would do.

GB200 is the previous NVL72 layout with B200 GPUs, about 192 GB each inside the rack instead of 288 GB, and ConnectX-7 rather than ConnectX-8. If the extra memory per GPU is unused, GB200 is the rack to price first.

Price and timing

A rack slice is not a single number on a card. Quantity, region, and how long you need the fabric change the ask. On this site the charge is still that ask, counted per second, once the machines are running. There is no separate monthly GB300 plan.

Vera Rubin is the following rack, with much higher per-GPU bandwidth if the preliminary spec holds. It is not a reason to idle a job that needs Blackwell Ultra this year.

Architectural Generation Comparison

Rack figures follow the NVL72 reference. An 8-GPU H100 node is listed only so the scale is obvious.

Spec	GB300 NVL72	GB200 NVL72	8× H100
GPUs	72 × B300	72 × B200	8 × H100
GPU memory	288 GB HBM3e each	192 GB HBM3e each	80 GB HBM3 each
Rack HBM	About 20 TB	About 13 TB	640 GB
CPU	36 × Grace	36 × Grace	x86 host
NVLink domain	About 130 TB/s	About 130 TB/s	Node NVLink only
NIC	ConnectX-8 · 800 Gb/s	ConnectX-7 · 400 Gb/s	ConnectX-7 class

Storage & Tooling Infrastructure

High-Speed Storage, Native Ports & Crypto Rails

Every NVIDIA GB300 NVL72 instance on NovaPod connects directly to high-throughput infrastructure designed to eliminate I/O bottlenecks and onboarding delays.

25 Gb/s Shared Network Storage

Mount large model checkpoints and training sets across one or multiple instances simultaneously. Data outlives single pods and stays accessible when instances stop.

JupyterLab, VS Code & SSH

Connect from your local terminal with standard SSH keys or open web-based IDEs directly in browser tabs without managing complex reverse proxy configs.

Non-Custodial Multi-Chain Rails

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Target Workloads for NVIDIA GB300 NVL72

Trillion-parameter training

One rack holds far more HBM than an 8-GPU node, so a very large dense or mixture-of-experts run can stay inside one NVLink domain.

1T-class pre-trainingMultimodal foundation modelsLarge post-training runsExpert pools that hate cross-rack traffic

Frontier FP4 inference

B300's dense FP4 rate plus the rack fabric is for high-concurrency serving of models that do not fit one HGX node.

Mixture-of-experts servingVery long contextsDisaggregated prefill and decodeHigh batch, not a single chat stream

Agent systems

Grace CPUs share a fast link with the GPUs, which helps loops that bounce between host tools and device weights.

Long reasoning tracesRetrieval next to the modelCode agents with long contextLess PCIe on every tool call

A slice, not the whole rack

Eight GPUs is the small unit. You still want this architecture when you specifically need Blackwell Ultra and Grace, not when any 8-GPU Hopper box would do.

8-GPU nodeHalf rack, 36 GPUsFull rack, 72 GPUsMulti-rack by quote

When to Choose NVIDIA GB300 NVL72

Pick GB300 for a rack

Ask for a slice from 8 GPUs up when the model does not fit one HGX node, or when you need the Grace link.

Pick a lone B300 for a short job

Same GPU, x86 host, no 20 TB domain. That is the per-second path.

Pick GB200 when 192 GB per GPU is enough

The fabric is similar, memory per GPU is lower, and the NIC is ConnectX-7 rather than ConnectX-8.

Do not confuse the name with B300

B300 is the chip. GB300 is 72 of those chips with 36 Grace CPUs.

B300 versus GB300

B300 is one GPU in an 8-way node on an x86 host. GB300 is the same silicon in an NVL72 rack: 72 GPUs, 36 Grace CPUs, one NVLink domain. Rent the GPU when the job fits a node. Ask for the rack when it does not.

How much to take

The small slice is 8 GPUs. Half a rack is 36. A full rack is 72. Multi-rack jobs are quoted, not clicked from a card.

Frequently Asked Questions

How is GB300 different from B300?

B300 is one Blackwell Ultra GPU, usually in an 8-way node on an x86 host. GB300 is that GPU inside an NVL72 rack with Grace CPUs and one NVLink domain.

What is the smallest order?

One 8-GPU node. Half a rack is 36 GPUs. A full rack is 72. Anything past one rack is a quote, not a card on the homepage.

How do we price a rack?

There is no fixed monthly GB300 plan. When a host lists a slice, the charge is that ask, counted per second, for as long as the machines run.

What is sparse FP4 versus dense FP4?

The rack is quoted near 1.44 EFLOPS sparse and about 1.08 EFLOPS dense. Use the dense number when the model cannot use sparsity.

Why Grace?

The CPUs sit on NVLink-C2C at about 900 GB/s, so orchestration and retrieval do not cross a slow PCIe hop on every step.

How does it compare with eight H100s?

Eight H100s are 640 GB of HBM3 and NVLink only inside that node. A GB300 rack is about 20 TB of HBM3e and a rack-wide NVLink domain. They are not the same purchase.

Is Vera Rubin the upgrade?

It is the following rack, with much higher per-GPU bandwidth if the preliminary spec holds. It is not a reason to idle a job that needs Blackwell Ultra this year.

Alternative Hardware Options

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