One-Click GPU Server Launch

The platform enables you to launch a GPU server in seconds with a single click. This removes the complexity of manual setup and configuration, allowing teams to get straight to work on their models and applications.

Business outcome: Reduces time-to-experiment from hours to seconds, accelerating development cycles.

VS Code Extension for Direct Connection

A dedicated VS Code extension allows developers to connect directly to a persistent GPU instance. This integrates the powerful cloud hardware into the developer's existing workflow, making it feel like a local environment.

Business outcome: Improves developer productivity by eliminating context switching and simplifying the development loop.

Snapshot-Based Persistent Storage

Users can pause an instance and restore the exact environment later via snapshots. This allows for persistent storage of work-in-progress without paying for continuous compute, as snapshots are stored at a lower cost.

Business outcome: Cuts costs on idle infrastructure while preserving the ability to resume work instantly.

Swap GPU Hardware on the Fly

The ability to swap GPU hardware or change specs like vCPU, RAM, and disk on an existing instance at any time provides unprecedented flexibility. Teams are not locked into a single configuration and can adapt to changing workload requirements.

Business outcome: Optimizes infrastructure spend by allowing teams to match hardware precisely to the task at hand.

Pre-Built Workflow Templates

One-click templates for popular tools like ComfyUI, Ollama, PyTorch, and CUDA Kernels simplify the setup of complex environments. This removes the friction of installing and configuring software stacks, making it accessible to a wider range of users.

Business outcome: Lowers the barrier to entry for specialized AI tasks, enabling faster deployment of standard workflows.

High-Speed Networking and No Egress Fees

With 7-10 Gbps networking and no data egress fees, the platform removes the hidden costs and bottlenecks often associated with cloud data transfer. This is critical for moving large datasets and models in and out of the cloud.

Business outcome: Provides predictable pricing and fast data movement, preventing unexpected bills and workflow slowdowns.

• Aggressive Cost LeadershipThe platform's core strength is its price point, which undercuts major hyperscalers like AWS and Azure by a significant margin for comparable hardware.
• True Usage-Based BillingThe per-minute billing model on top of hourly rates offers flexibility that is ideal for bursty or intermittent workloads, ensuring you never pay for idle time.
• Developer-First ExperienceFeatures like the VS Code extension, CLI tool, and one-click templates are designed to integrate seamlessly into a developer's workflow, reducing friction.
• Hardware FlexibilityThe ability to swap GPU types and adjust specs on an existing instance is a differentiator that allows teams to adapt to changing needs without re-provisioning.

• Unpredictable Monthly CostsWithout a flat-fee subscription option, budgeting can be less predictable for teams that prefer fixed monthly costs for infrastructure.
• Newer Infrastructure CompanyAs a smaller and newer company compared to hyperscalers, there is less of a track record for uptime and long-term reliability, which is a consideration for mission-critical workloads.
• Limited Enterprise SupportThe platform does not advertise enterprise-grade support SLAs or dedicated account management, which may be a requirement for larger organizations.
• Professional RealityThe most important thing to know is that while the cost savings are compelling, you are trading some of the stability, support, and compliance assurances of a hyperscaler for that lower price.

Bootstrapped Fine-Tuning

A data science consultancy can run fine-tuning jobs for clients on A100s at a fraction of the cost of major clouds, allowing them to offer competitive pricing and maintain healthier margins on projects.

Academic Research

An ML PhD researcher can use the platform to run experiments on high-end hardware without needing to secure large grants for cloud credits, making advanced research more accessible.

Generative AI Prototyping

A startup building with open-source models can use the one-click templates for ComfyUI or Ollama to quickly prototype and iterate on generative AI applications without deep infrastructure expertise.

Intermittent CI/CD for ML

An ML platform engineer at a seed-stage robotics company can use the per-minute billing to run GPU-accelerated tests in CI/CD pipelines, paying only for the minutes of compute used during each build.

Create an account on the Thunder Compute website and review the available GPU options and pricing on the /pricing page.

Use the web console or CLI tool to launch a new instance, selecting your desired GPU type (e.g., A100 or H100) and base configuration.

Connect to your instance using the VS Code extension or SSH via the CLI to begin setting up your environment and running workloads.

For persistent work, create a snapshot of your instance before pausing it. You can restore this exact environment later, paying only for snapshot storage in the interim.