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Wafer

Fastest inference for open models

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Wafer is an AI inference platform that profiles a model's full serving stack and continuously adjusts it to improve performance, reliability, and efficiency. It analyzes how a workload behaves in production, generates candidate configurations, and deploys only improvements that have been measured and verified against the target outcome.

The platform optimizes model, engine, kernels, and hardware together, covering batching, caching, routing, decode strategy, quantization, and expert sharding for MoE models. It supports NVIDIA B200/B300, AMD MI350X/MI355X, AWS Trainium, and Google TPUs, with custom kernels written for CUDA, HIP, Triton, and NKI. An agent profiles the stack to locate bottlenecks, generates and measures candidate configurations, and deploys the best-performing option while continuing to profile production traffic as load, models, and hardware change.

Wafer is aimed at teams serving AI models who need inference endpoints built around their specific model, traffic shape, and SLO. The platform offers a dedicated option for tailored deployments, and the site directs users to start building rather than listing specific plan tiers or pricing.

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