Wafer uses autonomous AI agents to profile and optimize GPU inference performance across production stacks.
Features
- AI-agent-driven inference diagnostics
- Full-stack optimization from kernels to models
- Improved GPU inference throughput and latency
- Fast bottleneck path identification
- Fits continuous optimization workflows for engineering teams
- Built for production inference deployment
Use Cases
- Pre-launch performance testing and tuning
- Cost control for online inference services
- Latency optimization under high concurrency
- Higher GPU resource utilization
- Productivity gains for inference platform teams
- Performance tuning for LLM and multi-model systems
FAQ
Wafer provides AI-agent-driven optimization for inference systems, analyzing and improving performance across the GPU stack so teams can find bottlenecks faster and ship high-performance model serving. Core capabilities include: AI-agent-driven inference diagnostics, Full-stack optimization from kernels to models, Improved GPU inference throughput and latency.
Common scenarios include: Pre-launch performance testing and tuning, Cost control for online inference services, Latency optimization under high concurrency.
