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Add a TorchServe example:
Why We Use TorchServe
TorchServe is designed to deliver high performance for serving PyTorch models, and it excels in the following key areas:
Batching Requests: TorchServe automatically batches incoming requests, processing multiple predictions in parallel. This reduces overhead, improves throughput, and ensures efficient use of resources, especially when dealing with large volumes of requests.
Horizontal Scaling: TorchServe allows for horizontal scaling, meaning it can easily scale across multiple machines or containers to handle increasing traffic. This ensures that the system remains responsive and can handle large volumes of inference requests without sacrificing performance.