Low stock

NVIDIA HGX B300: only 9 GPUs left at $4,899/mo, 25% below market.Only 9 B300 left at $4,899/mo

Configure B300

Solutions · AI labs

Private allocations for AI labs, priced below market.

Reserve a dedicated batch of B300, B200, H200 or MI355X GPUs for your team, with invoicing, a DPA and a named engineer, without a multi-year contract.

  • Servers from $19,592/mo
  • At least 25% below market
  • Root access in < 60 s
The problem

What slows teams down today

01

Multi-year commitments

The best rates elsewhere require 1 to 3-year contracts.

02

Capacity risk

Public stock can sell out before your next run starts.

03

Opaque pricing

Quotes vary and are hard to compare.

How CoreGPU fits

Dedicated GPUs, a monthly price, no surprises

Every server is racked, tested and imaged before it goes on sale, so you get root access in under a minute and a bill you know in advance.

  • Private allocations reserved for your team
  • Monthly renewal: no long-term lock-in
  • Invoices, a DPA and a named engineer
  • The same transparent price per GPU as our public catalog
Recommended servers

Configurations our engineers recommend

Live stock and the monthly price of the whole server. Open a GPU page to configure it.

Recommended

8× B3002.3 TB HBM3e · Blackwell Ultra

Blackwell Ultra nodes for frontier-scale inference and fine-tuning.

$39,192 /moOnly 9 left
Configure
Alternative

8× MI355X2.3 TB HBM3E · CDNA 4

288 GB per GPU on ROCm at about half the price of B300.

$19,592 /mo21 available
Configure
Alternative

8× H2001.13 TB HBM3e · Hopper

The best value for large fine-tunes on CUDA.

$21,592 /mo34 available
Configure
FAQ

AI labs: common questions

Another question? Our engineers answer within one business day.

Contact us
What is a private allocation?

A batch of GPUs reserved for you before it goes on public sale, billed monthly at our list price.

Do you sign DPAs and custom terms?

Yes. Our DPA is available, and we review reasonable custom terms for larger allocations.

How far ahead should we book?

Contact us as soon as you know your timeline; we can place you on the next allocation before it is announced.

Other solutions

Tell us about your workload

Model size, context length, deadline and budget: we reply with the right GPU and server count within one business day.

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