Neoclouds: The New GPU Clouds Changing AI Infrastructure

1. What Is a Neocloud?

A neocloud is a cloud company that focuses almost 100 percent on renting out high-end GPUs for artificial-intelligence work. Unlike the hyperscale clouds that sell hundreds of services, neoclouds keep their catalog small and center it on raw compute, bare-metal or thin-VM access, and fast networking. SemiAnalysis calls the category “a new breed of cloud compute provider focused on offering GPU rental” (source).

Key traits

• GPU-first: latest NVIDIA H100s, A100s, and soon Blackwell chips

• Very light virtualization for near-native speed

• Simple by-the-hour pricing

• Fast time to capacity – clusters in hours, not weeks

2. Why Are Neoclouds Growing Fast?

Scarce GPUs

Early 2024 saw on-demand access to H100s almost impossible to find; many teams still face long wait times on the big clouds (source).

Cost Savings

Neocloud rates average two to seven times lower than hyperscalers for the same silicon. Thunder Compute rents an on-demand A100 40 GB VM for $0.57 per GPU hour (source). By contrast, AWS charges $4.10 per GPU hour for its p4d A100 nodes (source).

Focus and Speed

Because they run only GPU clusters, neoclouds ship new hardware first and tune their networks for AI collective-communication patterns. This lets builders train larger models sooner and at higher throughput.

3. Neoclouds vs. Hyperscalers at a Glance

*Public on-demand prices, April 2025.

4. Pros and Cons

Advantages

• Lower cost per training hour

• Predictable performance thanks to direct GPU access

• Elastic capacity for bursty experiments

• Simple terms with less vendor lock-in

Trade-offs

• Fewer regions and compliance badges today

• Limited managed databases and event services

• You manage more of the stack yourself

5. How to Pick the Right Neocloud

1. Check GPU type and interconnect – If training at scale, look for current-gen cards on at least 400 Gbps InfiniBand or RoCE.

2. Inspect storage bandwidth – you want 250 GB/s aggregate or more.

3. Compare pricing models – on-demand for tests, reserved or spot for long runs.

4. Ask about network topology – fat-tree or rail-optimized designs cut congestion (source).

5. Verify support SLAs – 24 × 7 chat and a direct Slack or Discord channel help.

6. Run a one-day benchmark – fine-tune a known model and track tokens per second and total cost.

6. Quick Pricing Snapshot (April 2025)

Prices are public list rates – always confirm real-time quotes.

7. A Five-Step Action Plan

1. Define the job – model size, training days, budget cap.

2. Short-list three neoclouds with GPUs in stock.

3. Spin up a 4-GPU node and run your workflow end-to-end.

4. Track dollars per thousand training tokens as the metric.

5. Reserve capacity once you hit the target price-performance.

8. When to Stay on Your Current Cloud

If you need dozens of managed services, strict FedRAMP or HIPAA compliance in many regions, or deep integration with existing enterprise IAM, the big clouds may still be smoother. Many teams blend approaches – train on a neocloud, then deploy inference on AWS, Azure, or GCP.

9. Next Steps

Testing a neocloud is now easy. Thunder Compute offers instant A100 and H100 virtual machines starting at only $0.57 per GPU hour. Spin up a VM, move your data, and see if it beats your current bill. You can learn more at Thunder Compute.

Further Reading on Our Blog

• Cheapest GPU Cloud Providers for AI (2025)

• Should I Use GPU Cloud Spot Instances?

• Fine-Tune Llama 3 on a Single A100