Wafer offers an AI‑driven platform that automatically profiles, diagnoses, and optimizes inference workloads on any target hardware, achieving 1.5–5× higher throughput while reducing energy consumption. The solution provides a hardware‑agnostic API, cloud‑hosted performance dashboards, and SDKs for integration into deployment pipelines, serving semiconductor vendors, cloud providers, and AI research labs.
Funding
Funding not disclosed
Founders
Product
Problem
AI inference workloads often run slower than the underlying hardware’s capability, leading to higher latency and elevated compute costs. Deployments across heterogeneous hardware stacks require manual kernel tuning and architecture adjustments, which are time‑consuming and error‑prone.
Solution
Wafer delivers an AI‑driven inference optimization platform that automatically profiles, diagnoses, and accelerates model execution on any target hardware. Its autonomous agents instrument the full software stack, collect performance metrics, and apply machine‑learning‑based tuning to kernels and model graphs. The system generates optimized binaries and runtime configurations that achieve 1.5–5× higher throughput while reducing energy consumption. Results are streamed to a cloud analytics service where developers can monitor latency, cost per token, and performance‑per‑watt dashboards. Integration is provided via a hardware‑agnostic API and optional SDKs for ASIC vendors, cloud providers, and AI research labs, enabling end‑to‑end optimization without manual intervention.
Target Audience
Primary customers are semiconductor vendors, cloud infrastructure providers, and AI research laboratories that need to maximize inference efficiency across diverse hardware platforms.
Features
- Autonomous profiling agents that capture hardware counters, memory bandwidth, and GPU/TPU utilization in real time
- Reinforcement‑learning‑based kernel selection and graph rewrite engine that tailors execution to the specific ISA and memory hierarchy
- Hardware abstraction layer supporting ASICs, GPUs, TPUs, and emerging accelerators, eliminating the need for per‑device code paths
- Cloud‑hosted analytics pipeline with latency, cost‑per‑token, and performance‑per‑watt metrics exposed via RESTful APIs and Grafana‑style dashboards
- SDKs and CI/CD plugins for seamless integration into existing model deployment pipelines (Docker, Kubernetes, SageMaker, etc.)
- Role‑based access control and end‑to‑end encryption to meet enterprise security and compliance requirements
- Tiered SaaS offering (Starter, Pro, Max) with flat‑rate pricing and request quotas, plus custom licensing for enterprise‑scale agents