QuantaCloud

QuantaCloud GPU pricing overview

QuantaCloud is a bare-metal GPU cloud provider headquartered in United States. 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. Billing is Reserved / On-demand with a minimum commitment of Varies by config. Available regions include US. GPU instances can be provisioned on-demand, making QuantaCloud suitable for both short-duration experiments and sustained production workloads.

QuantaCloud vs other GPU providers

QuantaCloud competes with providers including Lambda Labs, CoreWeave, RunPod, Paperspace, Vast.ai, and the major hyperscalers (AWS, Google Cloud, Azure) for GPU compute workloads spanning LLM training, fine-tuning, and inference serving. Key differentiators include: Bare-metal performance; InfiniBand networking; Large cluster configs. Use the side-by-side comparison tool above to see QuantaCloud pricing against any other provider across shared GPU models. For a broader market view, the live GPU prices table shows all 6 QuantaCloud listings alongside 91+ providers in a single sortable view.

Best use cases for QuantaCloud

QuantaCloud is best suited for: Large-scale LLM training, Multi-node clusters, Reserved GPU capacity. Support tiers range from Standard → Enterprise, making it viable for both individual researchers and enterprise teams with SLA requirements. There are currently 6 active GPU listings on QuantaCloud, covering RTX A6000, NVIDIA RTX 6000 Ada Generation, NVIDIA L40, L40S and more. H100 SXM5 instances with NVLink interconnect are commonly used for large-scale training workloads. A100 40GB and RTX 4090 instances are frequently used for fine-tuning and inference of smaller models.

QuantaCloud billing model and cost structure

QuantaCloud uses Reserved / On-demand pricing. Spot pricing is not currently available on this provider — all instances are on-demand. Reserved instance pricing may be available for predictable long-running workloads — check the provider's pricing page for current committed-use rates. Always compare the effective hourly rate including egress, storage, and networking costs when evaluating total cost of ownership across providers.

Choosing the right GPU on QuantaCloud

GPU selection depends on model size, precision, and whether your workload is compute-bound or memory-bandwidth-bound. For LLM training above 30B parameters, H100 80GB SXM5 instances with NVLink are the standard choice — high HBM3 bandwidth and FP16 throughput make them well-suited for large transformer workloads. For inference of 7B–13B models in FP16 or BF16, A100 40GB is a common choice on many providers. RTX 4090 instances are widely used for fine-tuning, prototyping, and quantized inference (INT4/INT8) of models up to 70B. Read the H100 vs A100 guide or the GPU benchmarks for ML guide for a full breakdown.

How QuantaCloud pricing data is collected

Prices shown are sourced from QuantaCloud's public pricing API or pricing page and refreshed every 15 minutes. On-demand rates reflect the current list price for a single GPU instance in the cheapest available region. Spot prices, where available, reflect interruptible instance rates at the time of the last snapshot. All prices are in USD per hour. Daily snapshots are kept in our history archive and visualised in the GPU price history charts — useful for identifying seasonal pricing patterns and evaluating whether current rates are above or below the 30-day average.

Evaluating managed LLM inference APIs as an alternative to self-hosted GPU compute? Compare live LLM token prices across OpenAI, Anthropic, Google, Groq, and many other providers. The cheapest GPU cloud guide covers the break-even analysis between self-hosted and managed inference at different request volumes.