CoreWeave
Specialized GPU cloud built for large-scale AI training and inference
About CoreWeave
CoreWeave is a Kubernetes-native GPU cloud designed for AI labs and enterprises running large training and inference workloads. It offers tens of thousands of NVIDIA H100, H200, GB200, and A100 GPUs with InfiniBand networking, bare-metal performance, and reserved capacity contracts. Used by leading AI labs for foundation model training, it competes with hyperscalers on price-performance for serious GPU clusters.
Pros & Cons
Pros
- Massive GPU capacity at scale
- InfiniBand-class networking for distributed training
- Far cheaper than AWS/Azure for equivalent GPUs
Cons
- Best suited to teams comfortable with Kubernetes
- Not ideal for casual or small-scale researchers
- Long-term commitments needed for best pricing
Key Features
H100 & GB200 Clusters
Multi-thousand-GPU clusters with NVLink and InfiniBand for distributed training
Bare-metal Performance
Direct hardware access with minimal virtualization overhead
Kubernetes-native
First-class K8s scheduling for ML training and inference workloads
Reserved Capacity
Long-term contracts that guarantee GPU availability at negotiated rates
Pricing
- A100 / H100 / H200 GPUs
- Pay-as-you-go
- InfiniBand networking
- Discounted long-term rates
- Guaranteed capacity
- Dedicated support
Best For
Foundation model pre-training
Run multi-thousand-GPU H100 clusters with InfiniBand for distributed training.
Large-scale LLM fine-tuning
Reserve dense GPU capacity for weeks-long fine-tuning runs at predictable cost.
High-throughput inference
Serve production AI models on Kubernetes with autoscaling GPU nodes.
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