192 GB HBM3e/GPU · NVLink 5.0 · 8× SXM HGX nodes from 20+ vetted providers — ~30% less than hyperscale. Quotes in under 24 hours.
GB200 NVL pricing ranges from $10.00 to $18.00+ per GPU-hour depending on provider and contract type. The GB200 NVL72 rack delivers 72 Blackwell GPUs as a single logical system–$85/hr per node
| Provider | On-demand $/GPU-hr | GB200 NVL availability | Notes |
|---|---|---|---|
| AWS | ~$3.90 – $10.60 | Wait / limited | 8-GPU nodes only. Egress fees extra. |
| Google Cloud | ~$3.72 (spot) | Preemptible | Spot only. Can be interrupted. |
| Microsoft Azure | ~$6.98 – $13.78 | Limited regions | Most expensive. SLA-backed. |
| CoreWeave | ~$3.50 – $5.00 | Available | Enterprise. Reserved pricing only. |
| Lambda Labs | ~$2.49 – $3.99 | Available | No egress fees. Dev-focused. |
GPUaaS.com — wholesale ↓ UP TO 30% LOWER | ~$2.2 – $2.9 | In stock | Free matchmaking. Flexible commitment. |
Prices indicative as of May 2026. Hyperscaler rates from public pricing pages. Wholesale rates via GPUaaS.com vary by configuration and commitment term.
Train 70B–Trillion-parameter model training. The NVL72 rack architecture connects 72 Blackwell GPUs over NVLink 5.0 as a single unified memory domain — no multi-node communication overhead.
Real-time inference on the largest production models. GB200 delivers the highest throughput per rack of any available GPU system for serving 200B+ parameter models at production scale.— 40–80% more tokens/sec vs H100.
Full fine-tuning, LoRA, and QLoRA on models that exceed H100 memory. Larger batches, fewer gradient checkpointing hacks, faster convergence per dollar spent.
32k–Agentic AI and multi-modal foundation models. The unified memory architecture handles mixture-of-experts, multi-modal, and chain-of-thought workloads without memory fragmentation across nodes.
Pick the region for latency, compliance or sovereignty. We handle the matchmaking — you talk straight to the operator.
GPUaaS wholesale vs. cloud list price. Move the slider to your cluster size.
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Start simple — how many GPUs or nodes and what type — then add as much detail as you like. Inference or training. Model architecture. Precision. Virtualization type. Budgets and timelines.
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When we've found the perfect partner for your project, you'll get quotations for the GPU you need, usually within a few hours.
We'll smooth your ride through the provisioning process, and you can get on with your project.
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GPUaaS.com connects enterprises with vetted GPU providers for large-scale compute at up to 30% lower cost than hyperscalers.
