CloudGPU vs QuantaCloud: Pricing, Specs, Strengths & Weaknesses
Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for CloudGPU and QuantaCloud. Rates sourced from public APIs — verify current availability and billing terms before purchasing.
Overview
Strengths & Best For
CloudGPU rents on-demand GPUs billed per minute, with root SSH access and templates for Ollama, JupyterLab, ComfyUI and LLaMA-Factory. Prices come from CloudGPU's public price feed.
- Per-minute billing — pay only for what you use
- Root SSH access on every instance
- Ready-to-use templates: Ollama, JupyterLab, ComfyUI, LLaMA-Factory
- Wide consumer and data-center GPU selection
- Smaller provider — limited scale vs hyperscalers
- No enterprise SLAs or compliance certifications
- Region information not published per instance
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QuantaCloud provides bare-metal A100, H100, H200, and B300 GPU clusters with InfiniBand interconnect and no virtualization overhead, purpose-built for large-scale LLM training and multi-node distributed AI workloads. Reserved and cluster configurations are available for organizations that need dedicated GPU infrastructure with consistent performance for long-running training runs. A specialist bare-metal GPU cloud for AI labs and enterprises that need maximum cluster performance for frontier model training.
- Bare-metal performance
- InfiniBand networking
- Large cluster configs
- H200 and B300 availability
- Bare-metal focused — requires more infrastructure management
- Limited self-service tooling
- Smaller ecosystem vs cloud providers
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Live GPU Pricing
Green = lower price. On-demand hourly rates. Updated every 15 minutes.
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Key differences
CloudGPU
On-demand GPUs billed per minute — RTX 3090 to A100, root SSH, Ollama and ComfyUI templates
QuantaCloud
Bare-metal GPU cloud with A100, H100, and H200 clusters