Ray Serve
PulseAugur coverage of Ray Serve — every cluster mentioning Ray Serve across labs, papers, and developer communities, ranked by signal.
3 day(s) with sentiment data
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Ray Serve latency issue impacts MLOps performance
A developer discovered that Ray Serve, a model serving framework, introduced approximately 67 milliseconds of latency into their MLOps pipeline. This latency was not immediately apparent and was described as "quietly st…
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Anyscale details Ray Serve async inference for video-indexing service
Anyscale has detailed a practical implementation of its asynchronous inference feature within Ray Serve, demonstrating its use in a video-indexing service. This service leverages message queues like Redis or RabbitMQ fo…
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Google Ray Serve on TPUs simplifies multi-host AI inference
Google has demonstrated Ray Serve running on its Tensor Processing Units (TPUs), focusing on gang scheduling for multi-host models. This approach aims to simplify infrastructure complexity for scalable inference stacks …
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Anyscale cuts LLM serving costs with disaggregated prefill-decode on AMD
Anyscale has demonstrated significant cost savings in LLM serving by disaggregating the prefill and decode phases of inference. This approach separates prompt processing onto dedicated GPUs from token generation, reduci…
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Anyscale adds fault tolerance for MoE models in vLLM with Ray Serve
Anyscale has introduced a new fault tolerance feature for its vLLM serving engine, integrated with Ray Serve. This enhancement specifically addresses the challenges of deploying large Mixture-of-Experts (MoE) models, wh…