neoscale.ai — GPU compute at half the hyperscaler price

Live cost calculator

Price it out. No sales call required.

Drag to size your cluster. We'll show the delta vs. AWS p5, GCP a3-mega, and Azure ND H200 v5.

GPU model NVIDIA

GPU count 8

1128512

Runtime (hrs) 720

1h1 mo3 mo

Commitment

Your total USD

$20,131

$3.49 / GPU·hr · 8 GPUs · 720h

Compare

You save $23,467 vs. AWS p5.

That's 5 weeks of extra runway for the same budget.

See full pricing Reserve capacity →

Platform

Built for the three things
that actually matter.

01 PRICE

Transparent, flat, no markup.

One price, listed publicly. No "contact sales" to see B200 rates. No tiered data-transfer pricing. No surprise bills.

B200 on-demand$3.49/hr

Egress, anywhere$0.00

Storage (SSD)$0.04/GB·mo

02 SPEED

From signup to training in <5 minutes.

Clusters spin up in under a minute. Preconfigured with CUDA 12.5, PyTorch 2.5, NCCL, and Slurm. SSH and you're in.

03 FABRIC

3.2 Tb/s non-blocking InfiniBand.

NDR400 per GPU, rail-optimized topology. Multi-node training scales linearly to 1,024 GPUs in a single pod.

┌── POD ──────────────────────────┐
│  [GPU]─[GPU]─[GPU]─[GPU]     │
│     │     │     │     │        │
│  [SW1]═[SW2]═[SW3]═[SW4]     │
│     ║     ║     ║     ║        │
│     └─────┴──SPINE──┴────┘      │
└──────────────────────────────────┘

04 AVAILABILITY

Capacity you can count on.

4,200+ B200s online today, 12,000 coming by Q3. We publish live availability by region — so you know before you plan.

05 OBSERVABILITY

Per-GPU metrics, zero config.

SM utilization, VRAM, NVLink traffic, fabric errors — streaming to your dashboard, Prometheus, or Grafana Cloud.

06 SUPPORT

Real engineers. No tier-one triage.

Slack channel with our platform team. Median first response: 4 minutes. 24/7 for reserved customers.

Workloads

What teams are running on Neoscale.

All solutions →

Training

Pretraining LLMs up to 405B params

Reserved 512-GPU pods, fault-tolerant checkpointing, InfiniBand NDR fabric.

Inference

Low-latency serving at any QPS

Bare-metal or k8s, vLLM/TensorRT-LLM images preloaded. Autoscale on token throughput.

Fine-tuning

LoRA + full-weight runs

Spin up single-node 8×B200, run, checkpoint, tear down. Pay for minutes, not months.

Research

Slurm + Jupyter for academic teams

Shared quota, fair-share scheduling, and .edu pricing for university labs.

Developer experience

One CLI. One API.
Zero surprises.

Deploy a cluster in a single command. Terraform provider, Python SDK, and a REST API that makes sense. Docker, CUDA, and your favorite ML runtimes, ready on boot.

CLI REST Terraform Python SDK Go SDK k8s operator Slurm

# Launch a 16×B200 cluster, 48 hour reservation $ neoscale clusters create \ --gpu b200 \ --count 16 \ --region dfw-01 \ --reserve 48h \ --image nvidia/pytorch:25.04-py3 → cluster_id: cls_9kQ2mZ... → status: provisioning → estimated: 47 seconds → hourly: $55.84 (16 × $3.49) $ neoscale ssh cls_9kQ2mZ # you're on the head node. $ srun -N 2 -n 16 python train.py → [rank 0] initialized NCCL: 3.2 Tb/s → [step 100] 12,847 tok/s/gpu

Trusted by

184+ teams shipping on Neoscale

helion.ai

◆ parallax

ORBIT/LABS

nimbus_

cobalt

fenway·ml

dendrite

◎ quanta

fermi.co

syntek/

atlas·compute

grayscale

// customer note

"We moved our 32-GPU training runs off AWS in a week. Same hardware, same throughput, 52% lower bill. The only thing I miss is the 3-month quota wait."

Priya Narang

Head of Infra · Helion AI

Previous cloudAWS p5.48xlarge

Monthly spend (before)$412K

Monthly spend (after)$198K

Δ throughput+1.4%

Time to migrate9 days

// get started

The GPUs are idle
somewhere. Let's change that.

Launch an 8-GPU B200 cluster in about a minute. No credit card for the first hour.

Launch cluster → Talk to us

GPU compute, at half the hyperscaler price.