CoreWeave's customers include some of the most compute-intensive AI companies in the world. Here are the real-world use cases that explain why AI startups choose CoreWeave over traditional cloud providers.
About CoreWeave: CoreWeave is a specialised GPU cloud provider and NVIDIA strategic partner, offering H100, A100, and L40S GPU infrastructure purpose-built for AI workloads. Apply for access at coreweave.com.
Use Case 1: Large Language Model Training
Training foundation models requires thousands of GPUs running in tight synchronisation over weeks or months. CoreWeave's InfiniBand fabric and large-scale H100 availability make it the infrastructure of choice for companies training models in the 7B–70B parameter range.
Example scenario: A conversational AI startup training a 13B parameter domain-specific model for legal document analysis. Requirements: 64× H100 SXM GPUs, 400Gb/s InfiniBand, 10PB of NVMe-backed storage for training data. CoreWeave handles this scale routinely — provisioning this cluster in under 30 minutes versus the days-long wait on AWS Reserved capacity.
Use Case 2: High-Volume Inference Serving
Serving LLMs to end users requires consistent low latency at scale. CoreWeave customers serving production AI products need GPU infrastructure that scales with user load — often rapidly and unpredictably.
Example scenario: A B2C AI writing assistant with 500,000 active users. Peak usage requires 40 concurrent H100s; off-peak drops to 8. CoreWeave's Kubernetes-native autoscaling provisions and deprovisions GPU capacity in minutes, matching capacity to actual demand and reducing idle GPU spend by 60% versus always-on provisioning.
Use Case 3: Generative AI for Media and Entertainment
Text-to-video, image generation, and audio synthesis models are among the most GPU-intensive inference workloads. Companies like Stability AI, RunwayML, and similar startups use CoreWeave for high-throughput generative AI serving.
A single Stable Diffusion XL generation request requires significant GPU compute. Serving thousands of concurrent generation requests requires GPU farms that scale elastically. CoreWeave's L40S and A100 inventory is particularly suited to this workload due to the large VRAM requirements of generative models.
Use Case 4: AI-Powered Drug Discovery
Pharmaceutical and biotech companies use GPU clusters for protein folding simulations (AlphaFold-style workloads), molecular dynamics, and drug-protein interaction modelling. These workloads run continuously for weeks and require both high single-GPU performance and the ability to run many parallel simulations simultaneously.
CoreWeave's A100 80GB instances with NVLink are particularly suited for this use case — the large VRAM allows entire molecular simulation datasets to remain in GPU memory rather than being swapped to system RAM.
Use Case 5: Real-Time Computer Vision at the Edge
Security, manufacturing quality control, and retail analytics companies run computer vision inference on video streams. This requires GPUs that can process 30+ frames per second per camera across many simultaneous streams.
CoreWeave's L40S GPUs (optimised for inference) running in regional data centres enable low-latency computer vision APIs that can serve edge locations with sub-100ms latency for real-time applications.
Use Case 6: AI Voice and Audio
Real-time speech recognition, voice cloning, and text-to-speech synthesis require extremely low latency — users notice any pause in a voice interaction. CoreWeave customers building voice AI products use GPU inference endpoints optimised for the sub-100ms response times required for natural conversation.
The Common Thread
All these use cases share three characteristics that make CoreWeave the right choice over general-purpose clouds:
- Pure GPU compute — The workload is almost entirely GPU-bound, with minimal benefit from general cloud services
- High GPU density — They need many GPUs, not a few. CoreWeave's purpose-built infrastructure handles dense GPU deployments more efficiently
- Cost sensitivity — GPU costs represent 40–80% of total infrastructure spend, making CoreWeave's 20–35% cost advantage material
Notable CoreWeave customers include: Mistral AI, Cohere, Writer, CoreWeave reportedly powers parts of Microsoft's AI infrastructure, and numerous AI startups that raised $10M–$500M and need scalable GPU capacity without the complexity of building their own data centres.