NVIDIA Blackwell Ultra — B300 rental
Blackwell Ultra changed the math on AI compute, and the B300 puts 288 GB of that math on one card. On the big clouds, this card sits behind quotas and capacity reservations. With TheAI, one call and a short agreement let you rent B300 GPU capacity by the month, on machines nobody else touches. Assigned in days, not a procurement quarter.
288 GB
HBM3e per card
2,304 GB across the 8-card node
8 TB/s
Memory Bandwidth
Keeps large models fed, no read stalls
Native FP4
5th-gen Tensor Cores
~2x inference throughput vs FP8
In Days
To Assignment
Dedicated node after the first call
High-Performance Computing
Modern HPC looks less like physics codes and more like trillion-parameter training runs. The Blackwell architecture follows that shift, and the B300 tops it with 288 GB of HBM3e per card. An NVIDIA B300 GPU cluster keeps entire model states in memory, which is what the largest jobs in today's data centers starve for.
208B transistors
Two dies on Blackwell Ultra
FP4 Tensor Cores
~2x inference vs FP8
~8 TB/s memory
No read stalls at scale
Eight dedicated cards, single tenant, bare metal.
288 GB HBM3e per card — even trillion-parameter models fit on one node
Fifth-generation NVLink at 1.8 TB/s per GPU
Root access from the driver up, nothing shared
Reference system
The NVIDIA HGX B300 is the reference way to deploy this card: eight B300 GPUs in the SXM6 form factor, wired into one node. The numbers below come from NVIDIA's published materials:
GPUs
8× NVIDIA B300
Blackwell Ultra generation
GPU memory
2.3 TB
HBM3e in total, 288 GB per card
Memory bandwidth
8 TB/s
per GPU
DGX B300 inference
Up to 144 PFLOPS
FP4 sparse · 108 PFLOPS dense FP4
DGX B300 training
72 PFLOPS
FP8 sparse · 36 PFLOPS dense
Silicon
208B transistors
Two reticle-sized dies, TSMC 4NP
GPU interconnect
1.8 TB/s
5th-gen NVLink per GPU
Networking
8× ConnectX-8
800 Gb/s each
Storage
~30 TB
local NVMe (reference build)
The B300 is a specialist card, and B300 rental pays off fastest where memory decides the outcome. Below is the map, from AI training on large datasets to the inference patterns that keep a 288 GB card busy.
288 GB per card and 8 TB/s bandwidth make the B300 the default choice across frontier training and inference.
With 288 GB per card, full fine-tuning of 70B-class models runs comfortably inside a single node.
Agents carry state for hours, which makes memory capacity the real budget line.
Science on the B300 means AI-shaped science: foundation models trained on large experimental datasets.
A production RAG stack runs several models side by side, and the B300 hosts all of them without eviction.
Generative media is a memory story: frames are large and video models are larger.
One number, agreed up front
Your B300 rental price is a number we agree on before you sign, and it holds for the whole term. The invoice holds one line, because nothing is metered in the background. Spot price charts for Blackwell capacity jump around week to week; your rate holds for the whole term.
A large model wants the whole cluster, so that's the unit we rent. We build custom configurations from eight B300 cards upward and hand them over as reserved capacity, dedicated for your term. Instant clusters from shared pools work differently: ours take only a few days to assign, then run like your own machines.
An NVIDIA B300 GPU cloud instance here means a bare-metal node with root access and nothing virtual between your code and the silicon. Our GPU instances come under the same monthly agreement as clusters, so a single node can be where the project starts before it grows.
A short, human process
The whole process to rent B300 GPU capacity takes under one week, and most of it happens on our side. Here's how it goes:
Tell us the workload and we'll size the cluster together, from a single eight-card node to larger custom configurations. If you already know the shape, this step is one call.
We assign your GPU instances within days of the signed agreement and hand over root access. Your stack, your drivers, your container runtime; we stay out of the way.
Load the data and go, because the interconnect is already wired for training-scale jobs. From here the cluster stays assigned to you, month after month.
Buying a Blackwell Ultra node ties up capital for years, while renting turns the same hardware into a monthly operating expense.
Power, cooling, and failed-card replacements sit inside our rate, so your operational costs stay flat and predictable.
Monthly terms mean no long-term commitment: keep the cluster while the roadmap needs it, release it when it doesn't.
When the next NVIDIA generation ships, renters move to it under a new agreement while owners start a new procurement cycle.
Memory and bandwidth decide the fit. Here's the B300 next to the cards teams cross-shop.
Nobody hands out instant clusters of B300s, so we do the next best thing: dedicated machines within days of one call and a short agreement.
Our GPU pricing is a single monthly rate agreed before you sign, with no meters and no separate lines for storage or traffic.
Cards inside your cluster talk over a high-bandwidth interconnect, so gradient syncs don't tax every training step.
Real engineers answer 24/7 and treat a stuck driver at 3 a.m. as a normal ticket.
Your B300 rental runs on isolated, single-tenant hardware with guaranteed uptime, and your code, data, and weights stay yours alone.
TheAI works with teams anywhere in the world, and the only geography that matters is a connection to your cluster.
The rate is monthly and depends on cluster size and term length. We agree on the number before you sign, and it's the number you pay every month after.
One conversation, one agreement, hardware within days. Tell us the model and we'll size the cluster on the first call.