Rapt offers a model-defined GPU optimization platform that automatically reads the compute, memory, and bandwidth needs of each AI inference model in real time and right‑sizes GPU resources across the fleet. By continuously autoscaling GPU allocations millisecond by millisecond, it drives GPU utilization above 98%, eliminates manual tuning, and reduces inference compute costs for AI product and infrastructure teams.
Funding
Funding not disclosed
Founders
Product
Problem
AI inference workloads run continuously and must scale instantly with user demand, but GPU infrastructure was designed for static training workloads, leading to low utilization and high costs. Organizations often rely on manual tuning and static allocation, resulting in under‑utilized GPUs and wasted compute resources.
Solution
Rapt provides a model-defined GPU optimization platform that automatically reads the compute, memory, and bandwidth requirements of each AI model in real time. By continuously right‑sizing GPU resources across the entire fleet, Rapt ensures inference jobs run at peak performance while maintaining high GPU utilization. The platform eliminates manual tuning and static provisioning, dynamically adjusting allocations millisecond by millisecond to match workload fluctuations. This approach reduces inference compute costs dramatically and enables AI and infrastructure teams to scale services reliably without over‑provisioning.
Target Audience
Primary customers are AI product teams, inference engineers, and infrastructure operators who run large‑scale, latency‑sensitive inference services and need to maximize GPU efficiency and reduce operational costs.
Features
- Real‑time analysis of model-specific resource demands (compute, memory, bandwidth) for precise GPU allocation
- Automatic GPU autoscaling across the fleet, achieving 98%+ utilization compared to typical <35% levels
- Model‑defined optimization that adapts to diverse inference patterns, including LLM prefill, image generation bursts, and audio streaming
- No‑code integration that works with existing inference pipelines, removing the need for manual tuning
- Continuous monitoring and adjustment of GPU assignments to maintain peak performance under variable load