Fluidstack
Deploy large-scale GPU clusters for AI training and inference
Target Audience
- AI research labs
- Enterprise ML teams
- DevOps engineers managing GPU infrastructure
Hashtags
Overview
Fluidstack provides instant access to thousands of Nvidia GPUs for AI development teams. Offers fully managed cloud infrastructure with enterprise-grade support so engineers can focus on model building rather than hardware management.
Key Features
Massive Scale
Deploy clusters with 10,000+ GPUs and 1PB+ storage
Managed Infrastructure
Slurm/K8s clusters with 24/7 support and 15-min response
Latest Hardware
Immediate access to Nvidia H200/H100 GPUs with InfiniBand
Cost Savings
70% cheaper than hyperscale cloud providers
Use Cases
Train large AI models at scale
Deploy production inference clusters
Manage enterprise ML infrastructure
Reduce cloud compute costs
Pros & Cons
Pros
- World's largest available GPU clusters
- Includes 3.2T InfiniBand networking
- Dedicated engineering support team
- Substantial cost savings vs AWS/GCP
Cons
- 30-day minimum commitment for reserved clusters
- No short-term options for large clusters
- Requires enterprise-level budget
Pricing Plans
Large Scale GPU Clusters
monthlyFeatures
- 4,096+ GPU clusters
- 1PB+ shared storage
- 30+ day commitments
On-Demand GPU Instances
hourlyFeatures
- 1-100+ GPUs
- 5-minute deployment
- Hourly billing
Pricing may have changed
For the most up-to-date pricing information, please visit the official website.
Visit websiteFrequently Asked Questions
How quickly can I deploy a GPU cluster?
Can deploy 4,096+ GPU clusters within two days with full infrastructure management
What support is included?
24/7 support with 15-minute response times and 99% uptime SLA
How much can I save compared to AWS?
Customers report 70% cost savings versus hyperscalers
Reviews for Fluidstack
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