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Awesome GitHub RepositoriesWorkload-Type Tuners
Adjusting GPU, memory, timeouts, and autoscaling settings based on whether the workload is generative or predictive.
Distinct from Workload Density Optimization: Distinct from Workload Density Optimization: focuses on tuning resources per workload type (generative vs predictive), not maximizing density.
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Awesome Workload-Type Tuners GitHub Repositories
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- kubeflow/kfserving
kubeflow/kfserving
5,576Ver en GitHubKServe is an open platform for deploying and serving generative and predictive AI models on Kubernetes. It defines inference services as custom resources with declarative YAML specifications, enabling a Kubernetes-native approach to model deployment and lifecycle management. The platform leverages Knative-based serverless scaling for automatic scale-to-zero and revision management, and supports a pluggable serving runtime architecture that maps model formats to containerized execution environments. KServe distinguishes itself through model-aware autoscaling that scales replicas based on token
Adjusts GPU, memory, and autoscaling settings based on whether the workload is generative or predictive.
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