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Wafer is for engineering teams building AI products that need fast, reliable model responses under real-world traffic. It helps address the latency and performance bottlenecks that can affect applications such as voice agents, coding assistants, and other AI features that make repeated model calls. Teams can bring a model, their production traffic patterns, and performance targets to create a dedicated inference endpoint. Wafer profiles the serving workload and tunes configurations across the model, inference engine, kernels, and hardware, including choices such as batching, caching, and decoding. It checks candidate configurations for correctness and reliability, then continues profiling and optimizing as traffic or models change. Wafer may suit teams that want workload-specific model serving rather than managing inference performance tuning themselves.
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