GPU CLOUD / CAPACITY FIRST
Run training, inference and production AI workloads on high-performance GPU infrastructure built for speed, scale and availability.
NO RESERVATION QUEUE / HOURLY BILLING / DEMO DATA
GPU COMPUTE HARDWARE / ILLUSTRATIVE VISUAL
LIVE CAPACITY
US EAST / DEMO
NVIDIA H100
Available
NVIDIA H200
Available
NVIDIA B200
Limited
NVIDIA L40S
Available
REGION
US East
CLUSTER
8 × H100
GPU MEMORY
640 GB
FROM
$2.49 / GPU / hr
DEPLOY TIME
< 30 sec
Illustrative capacity and pricing for template demonstration.
18K+
GPUs online
Illustrative demo scale
12
Global regions
Illustrative footprint
99.99%
Platform uptime
Illustrative target
3.2 Tbps
Backbone capacity
Illustrative network
<30 sec
Average deploy time
Illustrative benchmark
02 / GPU INSTANCE EXPLORER
Choose your compute.
Match memory, bandwidth and cluster density to training, inference, fine-tuning and rendering workloads.
Training
Inference
Fine-tuning
Rendering
GPU INSTANCE / LIVE
Available
NVIDIA H100 SXM
Hopper
High-density training and production inference with NVLink-ready cluster options.
GPU MEMORY
80 GB HBM3
BANDWIDTH
3.35 TB/s
Large model training
FROM $
2.49
/ GPU / HR
Virginia, Texas, Singapore, Tokyo
GPU INSTANCE / LIVE
Available
NVIDIA H200 SXM
Hopper
More accelerator memory for long-context inference and large model fine-tuning.
GPU MEMORY
141 GB HBM3e
BANDWIDTH
4.8 TB/s
Memory-intensive AI
FROM $
3.29
/ GPU / HR
Virginia, Frankfurt, Tokyo
GPU INSTANCE / LIVE
Limited
NVIDIA B200
Blackwell
Flagship Blackwell capacity for teams planning large-scale training clusters.
GPU MEMORY
192 GB HBM3e
BANDWIDTH
8.0 TB/s
Next-generation training
FROM $
4.89
/ GPU / HR
Frankfurt
GPU INSTANCE / LIVE
Available
NVIDIA L40S
Ada Lovelace
A balanced option for serving models, visual workloads, and graphics pipelines.
GPU MEMORY
48 GB GDDR6
BANDWIDTH
864 GB/s
Inference and rendering
FROM $
1.49
/ GPU / HR
Texas, Singapore
03 / INFRASTRUCTURE
Every layer earns its place.
High-density clusters need more than fast accelerators. Racklane pairs GPU capacity with the network, storage and provisioning path behind it.
01
02
03
04
04 / ESTIMATE
Estimate your compute.
Select an instance, cluster size, duration and NVMe storage to see an illustrative hourly cost.
05 / COST COMPARISON
More compute. Less cloud tax.
Compare the same H100 cluster against a generic hyperscale cloud configuration. Figures below are fictional demo estimates.
MEASURE
RACKLANE
HYPERSCALER
8 × H100 / hr
$19.92
$31.80
Egress
Included
Extra
Deployment
Seconds
Minutes
Commitment
None
Often required
37%
Illustrative savings on long-running AI workloads. Actual quotes depend on configuration and usage.
06 / GLOBAL CAPACITY
Deploy where capacity is available.
Choose by GPU type, capacity status, region code and indicative latency, rather than office location.
US-E1
High availability
Virginia
United States
East-coast training and inference clusters with dense H100 capacity.
AVAILABLE GPUS
H100 · H200
12 ms
US-C1
Available
Texas
United States
Central US capacity for balanced training, graphics, and inference.
AVAILABLE GPUS
H100 · L40S
28 ms
EU-C1
Limited
Frankfurt
Germany
European high-memory and Blackwell capacity for regional workloads.
AVAILABLE GPUS
H200 · B200
34 ms
AP-S1
Available
Singapore
Singapore
Southeast Asia capacity for regional serving and model development.
AVAILABLE GPUS
H100 · L40S
41 ms
AP-N1
Available
Tokyo
Japan
High-memory GPU deployments near latency-sensitive Japan workloads.
AVAILABLE GPUS
H200
18 ms
07 / DEPLOYMENT
From zero to cluster in seconds.
The deployment path is predictable: choose the accelerator, select capacity, size the cluster, then provision.
01
Choose GPU
Memory and bandwidth
02
Select region
Check live capacity
03
Configure cluster
GPU count and storage
04
Deploy
API, CLI or console
$ racklane deploy h100 --gpu 8
Provisioning cluster…
Network ready
Storage attached
8 × H100 online
✓ Cluster ready in 27s
Illustrative terminal output for this template.
08 / WORKLOADS
Built for actual workloads.
GPU type is only one decision. Network topology, storage throughput and regional capacity matter to the job.
Model Training
Scale distributed jobs across dense accelerator pods.
GPU FIT
H100 / H200 multi-node
Private 400G east-west fabric
High-throughput NVMe checkpoints
Inference
Serve production traffic close to users with predictable capacity.
GPU FIT
L40S / H200
Regional low-latency serving
Fast model and cache reads
Fine-tuning
Adapt large models without reserving an entire fleet.
GPU FIT
H100 / H200
Private node-to-node links
Checkpoint and dataset NVMe
Image / Video Generation
Run batch generation pipelines around available GPU windows.
GPU FIT
L40S / H100
Burst-friendly private networking
Asset and output NVMe
HPC
Execute simulation and numerical workloads on scalable GPU clusters.
GPU FIT
H100 / B200
High-bandwidth cluster fabric
Parallel scratch storage
Rendering
Burst visual production jobs without fixed on-premises capacity.
GPU FIT
L40S
Regional asset access
NVMe asset staging
09 / ENTERPRISE
Infrastructure for production AI.
Reserved capacity, isolation and architectural support help teams move from experiments to dependable services.
Private GPU clusters
Isolated accelerator pods
Dedicated capacity
Reserved GPU windows
VPC networking
Private routing controls
SLA options
Scoped service commitments
Priority support
Architecture escalation
Security controls
Access and network policy
Custom architecture
Deployment consultation
10 / CUSTOMER STORY
Capacity changes the schedule.
A fictional example of how a training team could deploy a multi-node workload without a long reservation cycle.
VANTA LABS and all results are fictional template demo content.
CAPACITY / ON DEMAND
Your next cluster is ready.
Deploy high-performance GPU infrastructure without waiting weeks for capacity.