GPU Server Rental · Capacity on Request
Tell us the GPUs, cluster size and term you need. GR1D sources dedicated GPU servers and clusters for AI inference and training, then comes back with availability, terms and pricing in writing.
Request NVIDIA H100, H200 or Blackwell-class capacity by the server, the cluster or the rack. Dedicated hardware for your team, not shared instances or spot capacity.
Sourced to Your Spec
GPU model, count, interconnect, region and term, matched to your workload
Dedicated, Not Shared
Bare-metal servers or whole clusters reserved for your team
Terms in Writing
Availability, start date and pricing quoted before you commit
Own Your GPUs? Host Them
Bring your servers to GR1D's liquid-cooled colocation from $180/kW/month
See Host Servers (GPU colocation)
Use Cases
Whether you're serving inference in production or training a model, tell us the workload and we'll source dedicated hardware that fits it.
Dedicated GPU servers for production inference: LLM serving, image generation, recommendation and real-time AI, with steady latency on hardware no one else shares.
Multi-node clusters with a high-bandwidth interconnect for training and fine-tuning, reserved for your run instead of spot capacity that can be preempted.
Consistent GPU performance for your product's backend, on servers reserved for your team rather than shared cloud instances.
Single-tenant hardware for teams with isolation or data-residency requirements. Put the rules you work under in your request and we source to them.
Capacity for experiments, benchmarks and proofs of concept, quoted for the weeks you need it.
Running steady GPU workloads in the cloud? Get a dedicated quote to compare against your current bill before you renew.
Buyer's Checklist
The same GPU can cost very different amounts depending on the term, the network and the fine print. Ask every provider, us included, for these in writing.
H100, H200 and Blackwell-class GPUs differ in memory and throughput, so the right one depends on your model.
GR1D quotes: The exact GPU and server configuration
Shared and spot instances can be slowed by neighbors or preempted mid-job.
GR1D quotes: Dedicated servers or whole clusters
Multi-node training needs a high-bandwidth fabric between servers, not just fast GPUs.
GR1D quotes: Fabric type and bandwidth per GPU
Hourly, monthly and reserved terms price very differently for the same GPU.
GR1D quotes: Price on your term, in writing
Egress and data-transfer charges can add a large hidden cost to the monthly bill.
GR1D quotes: Data-transfer terms up front
Latency and data-residency rules depend on where the servers sit.
GR1D quotes: Region and facility before you sign
Hardware fails; how fast someone responds decides how long a job stalls.
GR1D quotes: Support hours and response times
Process
01
GPU model and count, training or inference, term, start date and region. The more specific, the faster the match.
02
GR1D finds dedicated servers or clusters that match your spec and checks them against your requirements.
03
Availability, start date, pricing and data-transfer terms, before you commit to anything.
04
Add capacity as workloads grow, or bring your own GPUs into GR1D's liquid-cooled colocation.
Questions
GR1D takes requests for dedicated GPU servers and clusters for AI inference and training, from NVIDIA H100 and H200 to Blackwell-class B200, GB200 and GB300. Tell us the GPU, count, term and location you need, and we'll source capacity and come back with availability, terms and pricing.
Host Servers is colocation: you bring your own GPU hardware and we provide space, power and liquid cooling from $180/kW/month. Rent Compute is for teams that want GPUs without buying them: tell us what you need, and GR1D sources dedicated capacity and quotes it to you.
By GPU model, server or cluster size, interconnect, term and location. Reserved terms usually cost less per GPU-hour than on-demand, and data-transfer and support terms change the total. GR1D quotes each request in writing before you commit.
More answers on the FAQ page.
Go deeper
Get Started
Tell us what you need and we'll come back with availability, terms and pricing. No commitment.
Discuss GPU models, cluster size, terms and timing with our infrastructure team.
GPU model and count
Training, inference or both
Term and start date
Region or data-residency needs
Interconnect and storage needs
Budget range (optional)
Or email us directly
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