JarvisLabs AI
Rent powerful GPUs like H100s and A100s in seconds for AI development and deep learning experiments.
Overview
JarvisLabs AI offers a streamlined platform for renting high-performance GPUs, designed specifically for AI development and deep learning. Users can quickly provision top-tier GPUs like H100s, H200s, A100s, and RTX cards in under 90 seconds, making it ideal for time-sensitive projects and rapid experimentation. The service operates on a pay-per-minute model, ensuring users only pay for the computational resources they actively consume, without the need for long-term contracts or upfront commitments. The platform simplifies the setup process by offering pre-configured templates for popular AI frameworks such as PyTorch, TensorFlow, ComfyUI, and Automatic1111. This allows developers to jump straight into their work without the hassle of environment configuration. JarvisLabs AI also provides various access methods, including JupyterLab, VS Code Web, and SSH, alongside options for deploying applications using tools like Gradio, Streamlit, and FastAPI, or custom endpoints, facilitating both development and sharing of AI projects.
Pricing
Managed Workbench Instances - H200 SXM
- 1 H200 SXM GPU
- 141GB VRAM
- 200GB RAM
- 16 vCPUs
- Minute-level billing
- Pre-built stacks: JupyterLab, VS Code, API endpoint & SSH
Managed Workbench Instances - H100 SXM
- 1 H100 SXM GPU
- 80GB VRAM
- 200GB RAM
- 16 vCPUs
- Minute-level billing
- Pre-built stacks: JupyterLab, VS Code, API endpoint & SSH
Pros & Cons
Pros
- Extremely fast deployment of GPU instances (under 90 seconds)
- Flexible pay-per-minute pricing eliminates long-term commitments
- Wide selection of high-end GPUs suitable for demanding AI tasks
- Pre-configured environments simplify setup for common ML frameworks
- Multiple development and access options cater to different workflows
- Scalable resources for both individual developers and larger teams
Cons
- Requires familiarity with deep learning frameworks and GPU computing
- Cost can accumulate quickly for continuous, long-running tasks if not managed
- Reliance on external platform for infrastructure, less control than self-hosting
- Specific GPU availability might vary based on demand
- No explicit mention of free tier or trial period for new users
Use Cases
Reviews & Ratings
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Best For
- Fine-tuning Large Language Models (LLMs)
- Running Stable Diffusion and other generative AI models
- Training complex deep learning models
- Developing and experimenting with AI applications
- High-performance computing for research and development
- Deploying AI models via custom endpoints
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