80 GB HBM3 · 3.35 TB/s · 8× SXM HGX nodes from 20+ vetted providers — ~30% less than hyperscale. Quotes in under 24 hours.
H100 cloud pricing ranges from $1.80 to $6+ per GPU-hour depending on provider and contract type. Hyperscalers bundle H100 in 8-GPU nodes at $50–$85/hr per node
| Provider | On-demand $/GPU-hr | H100 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–LLM training up to 70B parameter models on a single node. 80 GB HBM3 handles most production training workloads without multi-node overhead.
Serve production LLM traffic at scale. 3.35 TB/s bandwidth sustains high token throughput across multi-tenant inference workloads.— 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–128k context windows fit comfortably in HBM3e. Vector search, retrieval pipelines, and multi-modal inference run without memory-pressure fallbacks.
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.
Tell us the essentials. We'll line up real quotes from vetted wholesale providers — direct, no platform fee.
No need to crawl through GPU marketplaces. The world's best wholesale GPU providers are right here.
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.
Get the best GPU deals →We do the legwork, and find providers with capacity that fits your need. Our network includes:
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.
