01
B300
Blackwell Ultra · 288 GB HBM3e
- Performance14 PFLOPS
- Bandwidth8 TB/s
From
$7.00/GPU·hr
Limited
GPU Cloud
The AI developer
cloud, on demand.
Pods for building. Clusters for scaling. Reserved capacity for shipping. B300, B200, H200, H100, and A100 — billed by the second, NVLink-ready, in 12 regions.
<90s
Provisioning
12
Regions
99.99%
Uptime SLA
1s
Billing increment
The lineup
Every NVIDIA class, ready to deploy
From Ada Lovelace inference at $1.49 / GPU·hr to Blackwell Ultra B300 for frontier training — every node is NVLink-capable, NVMe-backed, and billed by the second.
Deploy
02
B200
Blackwell · 192 GB HBM3e
- Performance10 PFLOPS
- Bandwidth8 TB/s
From
$5.49/GPU·hr
Limited
03
H200 SXM
Hopper · 141 GB HBM3e
- Performance3,958 TFLOPS
- Bandwidth4.8 TB/s
From
$3.99/GPU·hr
Limited
04
H100 SXM
Hopper · 80 GB HBM3
- Performance3,958 TFLOPS
- Bandwidth3.35 TB/s
From
$2.99/GPU·hr
In stock
05
H100 NVL
Hopper · 94 GB HBM3
- Performance3,958 TFLOPS
- Bandwidth3.9 TB/s
From
$2.59/GPU·hr
In stock
06
A100 SXM
Ampere · 80 GB HBM2e
- Performance312 TFLOPS bf16
- Bandwidth2 TB/s
From
$1.89/GPU·hr
In stock
07
L40S
Ada Lovelace · 48 GB GDDR6
- Performance733 TFLOPS
- Bandwidth864 GB/s
From
$1.49/GPU·hr
In stock
Reserved capacity available · 1mo+ commitments
Talk to salesHow it works
From zero to inference in four steps
No replatforming. No lock-in. No hyperscaler tax. Bring your container, your framework, your code — we handle the rest.
01
Spin up
Pick a GPU, pick a region, pick a base image. Pod is ready with SSH and a public IP in under 90 seconds.
02
Build
Train, fine-tune, or batch-process. Your containers, your framework, your weights — persistent NVMe volumes follow your jobs.
03
Deploy
Push to production with templated inference endpoints, blue-green rollouts, and built-in TLS + auto-scaling.
04
Scale
Single pod to thousand-GPU clusters across 12 regions. Reserve capacity for predictable load, burst on demand for spikes.
Workloads
Picked for the work you actually do
Not sure which GPU to start with? Match the workload to the silicon — and start with a single node, scale to a cluster when the job demands it.
01
LLM training & pretraining
Frontier model training, full fine-tunes, and large-scale pretraining on multi-node clusters with NVLink fabric.
Recommended
B300B200
B300B200
Fine-tuning & adaptation
LoRA, QLoRA, full-parameter tunes, and RLHF on 7B → 70B+ models with shared NVMe checkpoint volumes.
RecommendedH100 SXMH100 NVL
H100 SXMH100 NVL
Production inference
High-throughput LLM serving with vLLM, TGI, or TensorRT-LLM. Sub-100ms first-token latency at scale.
RecommendedL40SH100 NVLH200 SXM
L40SH100 NVLH200 SXM
Diffusion, vision & multimodal
SDXL, FLUX, SVD, vision encoders, and embedding pipelines on memory-flexible Ada and Hopper class GPUs.
RecommendedL40SA100 SXM
L40SA100 SXM
Per-second billing
Pay only for time the GPU is up. Stop a pod, billing stops within the second.
NVLink + InfiniBand fabric
900 GB/s GPU-to-GPU bandwidth on SXM nodes, 3.2 Tbps east-west on reserved clusters.
Persistent NVMe volumes
Checkpoint, dataset, and weight volumes that survive pod restarts and follow your jobs.
Single pod to 1,000+ GPUs
Start with a single accelerator, scale into reserved multi-node clusters when you need to.
Need bigger?
Reserve a cluster.
Multi-node H200, B200, and B300 clusters with NVIDIA NVLink 5.0 fabric, dedicated capacity, and committed pricing. From a single 8-GPU node to thousand-GPU training runs — we handle the rest.
Multi-node NVLink fabric
8× B300 SXM per node · 1.8 TB/s GPU-to-GPU bandwidth
Reserved pricing
Up to 60% off on-demand · 1-mo to 3-yr commitment
Dedicated support
Priority access, 24/7 coverage, and SLA-backed reliability
Rapid provisioning
Clusters ready in hours, not days
Custom networking
Tailored VPC, routing, and isolation to fit your architecture
99.99% uptime SLA
Enterprise-grade reliability you can build on
Ready to build?
Talk to our infrastructure team and get a custom quote.
Cluster configuration
Review & requestNVIDIA B300 SXM · 8-node cluster
NVLink fabric · Redundant power · PCIe 5.0
GPUs
64× NVIDIA B300GPU memory
18.4 TB (288 GB / GPU)Interconnect
NVLink Switch System 5.0vCPUs / node
96 vCPUsNetworking
800 Gbps · RDMATerm
12-month reserved
Secure·Private·Enterprise ready
FAQ
Common questions
01How fast can I get a GPU?
On-demand pods provision in under 90 seconds for available capacity (H100, A100, L40S). Limited-stock SKUs like B200 and B200 typically provision in a few minutes. Reserved multi-node clusters are provisioned in under 24 hours after sales handoff.
02What's included in the per-hour price?
The GPU(s), bundled vCPU and system RAM, the container disk, public IP, ingress and egress bandwidth (with a generous monthly allowance), and persistent NVMe storage up to a quota. Egress beyond the allowance and reserved capacity are billed separately.
03Can I run multi-node training jobs?
Yes. SXM nodes are NVLink-fabric-connected within a chassis (900 GB/s GPU-to-GPU). Reserved clusters add InfiniBand or RoCE east-west networking (up to 3.2 Tbps) across nodes — purpose-built for distributed training with FSDP, DeepSpeed, or NVIDIA's Megatron-LM stack.
04How does reserved pricing work?
Commit to capacity for 1 month to 3 years and save up to 60% off the on-demand rate. Reserved capacity is dedicated, region-pinned, and SLA-backed. Talk to sales for a quote tailored to your training schedule.
05Do you support spot pricing for interruptible jobs?
Yes. Spot pods are roughly 50–70% cheaper than on-demand and are interruptible with 60 seconds of notice. Best fit for fault-tolerant training with checkpointing, hyperparameter sweeps, and batch inference.
06Which frameworks are pre-installed?
CUDA 12.4, PyTorch 2.x, JAX, TensorFlow, vLLM, TGI, TensorRT-LLM, and common scientific stacks ship in base images. You can also bring your own Docker image from any public or private registry.
Ready when you are
The AI developer cloud
No replatforming. No lock-in. No hyperscaler tax. Pick a GPU, pick a region, and you're running.