1 repositorio
Awesome GitHub RepositoriesAsynchronous Gating
Flow-control mechanisms that queue and dispatch offline inference requests based on hardware availability.
Distinct from Inference Batching: Distinct from Inference Batching: focuses on the queuing and gating of asynchronous jobs rather than the grouping of requests for a single compute pass.
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Awesome Asynchronous Gating GitHub Repositories
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- llm-d/llm-d
llm-d/llm-d
2,514Ver en GitHubllm-d is a distributed serving framework designed for large language model inference. It functions as an inference orchestrator and gateway, providing a control plane for deploying model replicas and managing hardware accelerators. The system includes a batch inference scheduler and a cache manager to coordinate request flow and memory utilization. The project is distinguished by a disaggregated serving architecture that separates prefill and decode execution phases across specialized workers to maximize throughput. It employs a hardware-agnostic control plane and tiered cache offloading, mov
Implements flow-control gating to execute offline batch requests using spare hardware capacity.
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