# Rapt.AI

> Model-defined GPU optimization platform for AI inference.

Rapt.AI is the industry's first model-defined GPU optimization platform. It reads each AI model's real-time compute, memory, and bandwidth requirements and dynamically allocates GPU resources to match — achieving 98%+ GPU utilization versus the lower than 35% industry average, with up to 90% infrastructure cost reduction. Deploys in 5 minutes on existing Kubernetes clusters with no migration required.

Canonical URL: https://rapt.ai/
Contact: https://rapt.ai/demo

## Pages

- [Homepage](/): Platform overview, capabilities, and customer results.
- [About](/about): Company mission, leadership team, and values.
- [Advisory Board](/about/board): Strategic Advisory Board members.
- [Start a Pilot](/demo): Request a GPU optimization pilot or meeting.
- [Enterprise Solutions](/solutions/enterprise): Enterprise GPU optimization for AI teams at scale.
- [Cloud Solutions](/solutions/cloud): Multi-tenant GPU infrastructure for cloud providers.
- [Startup Solutions](/solutions/startups): GPU cost optimization for AI startups.
- [ML Engineers](/solutions/ml-engineers): GPU optimization for ML engineering teams.
- [Platform Leads](/solutions/platform-leads): GPU orchestration for platform engineering.
- [CTOs and Founders](/solutions/ctos-founders): GPU strategy for technical leadership.
- [Finance Ops](/solutions/finance-ops): GPU cost management for finance teams.
- [ROI Calculator](/calculator): Estimate GPU infrastructure cost savings.
- [Insights](/insights): Technical articles, GPU infrastructure research, industry analysis.
- [Product Demo](/product-demo): Live product demo — dashboard, model management, analytics.
- [Playground](/playground): Interactive GPU orchestration simulator.
- [GPU Infrastructure](/gpu-infrastructure): GPU infrastructure planning guide.
- [FAQ](/faq): Frequently asked questions about Rapt.AI.

## Key Facts

- 98%+ GPU utilization (vs. lower than 35% industry average, Wharton Business School 2025)
- Up to 90% GPU infrastructure cost reduction
- 5-minute deployment on existing Kubernetes clusters
- Supports NVIDIA H100, A100, L40S, B300 and AMD GPUs
- Works across AWS, GCP, Azure, on-prem, and hybrid environments
- Supports LLMs, vision models, audio/video models, MoE architectures, embedding models

## Social

- [LinkedIn](https://www.linkedin.com/company/rapt-ai/)
- [YouTube](https://www.youtube.com/@rapt-ai)
- [X/Twitter](https://x.com/rapt_ai)
- [Substack](https://substack.com/@raptai)

## License

Content on this site is © Rapt.AI. All rights reserved.