PRODUCT / GPU CLOUD
Deploy H100, H200, B200 and L40S capacity for training, fine-tuning, inference, rendering and high-performance compute. Choose a region, then scale the configuration around your workload.
REFERENCE ARCHITECTURE / 8 × H100
NODE 01
2 × H100 SXM
NODE 02
2 × H100 SXM
NODE 03
2 × H100 SXM
NODE 04
2 × H100 SXM
PRIVATE FABRIC
400G OPTIONS
NVMe storage → private VPC → API / CLI / Kubernetes
Diagram and figures are illustrative demo content.
MULTI-GPU CLUSTER / ILLUSTRATIVE VISUAL
01 / ACCELERATORS
Choose by workload, not logo.
Memory, bandwidth and hourly price are visible together so buyers can shortlist the right accelerator before requesting capacity.
NVIDIA H100 SXM
Hopper
80 GB HBM3
3.35 TB/s
$2.49 / GPU / hr
NVIDIA H200 SXM
Hopper
141 GB HBM3e
4.8 TB/s
$3.29 / GPU / hr
NVIDIA B200
Blackwell
192 GB HBM3e
8.0 TB/s
$4.89 / GPU / hr
NVIDIA L40S
Ada Lovelace
48 GB GDDR6
864 GB/s
$1.49 / GPU / hr
Prices and availability are fictional demo values. All accelerator configurations depend on live capacity.
02 / CONFIGURATIONS
Scale from one GPU to a pod.
A single accelerator keeps experiments lean. Multi-GPU nodes and private clusters support larger checkpoints and distributed training.
SINGLE INSTANCE
Start with one accelerator.
A focused instance for inference, prototyping, render jobs and fine-tuning.
1–2 GPUs
Regional placement
NVMe storage
API / CLI deploy
MULTI-GPU CLUSTER
Grow into a private pod.
A dedicated configuration for model parallelism, distributed jobs and predictable capacity.
4–8 GPUs / node
Private 400G options
Shared checkpoint path
VPC network policy
03 / CLUSTER FABRIC
The infrastructure between GPUs matters.
Private east-west networking and NVMe tiers keep large data movements close to the accelerators.
GPU COMPUTE
H100 / H200 / B200 / L40S
INTERCONNECT
Private 400G options
STORAGE
NVMe scratch + checkpoint
CONTROL PLANE
VPC / K8s / API / CLI
04 / OPERATE
Use the interface your team already runs.
Provision through a console, script deployments with the CLI or API, and orchestrate production services on Kubernetes.
Kubernetes
Schedule GPU workloads across private clusters with familiar deployment manifests.
kubectl apply -f workload.yaml
API
Automate instance lifecycle, capacity checks and cluster configuration.
POST /v1/clusters
CLI
Deploy and inspect compute from the terminal without waiting through a console flow.
$ racklane deploy h100 --gpu 8
05 / WORKLOAD FIT
Build for the job in front of you.
Training, inference, fine-tuning, generation, HPC and rendering call for different infrastructure choices.
Model Training
H100 / H200 multi-node
Scale distributed jobs across dense accelerator pods.
Inference
L40S / H200
Serve production traffic close to users with predictable capacity.
Fine-tuning
H100 / H200
Adapt large models without reserving an entire fleet.
Image / Video Generation
L40S / H100
Run batch generation pipelines around available GPU windows.
HPC
H100 / B200
Execute simulation and numerical workloads on scalable GPU clusters.
Rendering
L40S
Burst visual production jobs without fixed on-premises capacity.
CAPACITY / ON DEMAND
Ready to configure a cluster?
Request a region and GPU type, then size your compute around current capacity.