FluidStack
AI cloud platform optimized for training and inference using NVIDIA GPUs, supporting scalable machine learning workloads.
About FluidStack
Fluidstack is a premier AI cloud platform that enables rapid training and inference with immediate access to thousands of NVIDIA GPUs, including H100 and A100 models. Designed for enterprises and AI researchers, it facilitates large-scale model development and deployment. The platform offers fully managed infrastructure utilizing Slurm and Kubernetes, ensuring high availability, scalability, and reliable support with 15-minute response times and 99% uptime. Users can deploy extensive GPU clusters or launch on-demand GPU instances in under 5 minutes, streamlining AI workflows and reducing operational complexity.
How to Use
Reserve large-scale GPU clusters for extensive AI training and inference, or quickly launch on-demand GPU instances. The platform supports managed Kubernetes and Slurm environments, with dedicated engineering support available upon request.
Features
- On-demand GPU instances for flexible scaling
- 99% uptime service guarantee
- Managed infrastructure with Kubernetes and Slurm
- Access to thousands of NVIDIA GPUs including H100, A100, H200, and GB200
- Large GPU clusters optimized for training and inference
- Around-the-clock support with rapid 15-minute response times
Use Cases
- Managing scalable GPU clusters
- Performing large-scale AI inference
- Training complex deep learning models
- Accelerating machine learning development
- Deploying AI models in production environments
Best For
Pros
- Reduces operational complexity with fully managed infrastructure
- Immediate access to a broad range of NVIDIA GPUs
- Reliable high availability and quick support responses
- Potential cost savings compared to hyperscale cloud providers
- Supports large-scale AI workloads with scalable GPU clusters
Cons
- Minimum reserved cluster term of 30 days applies
- Pricing details for H200 on-demand instances require direct inquiry
- On-demand GPU availability is limited to over 100 GPUs