llm-d 1.0 adds production controls for distributed AI inference
IBM says the CNCF-governed project now supports multi-tenant quality of service, failover and rolling upgrades, building on Carlos Costa's push to make AI serving cloud-native.
By RuntimeWire Staff · Published
Primary source: IBM Newsroom
Why it matters
Inference infrastructure determines whether organizations can run AI services reliably and affordably at scale. llm-d's 1.0 release targets the production operations between a successful demo and a reliable service, while its cost and throughput claims remain tied to specific company-reported tests.
Carlos Costa spent years working on large-scale supercomputers before helping create llm-d to bring cloud-native methods to the problem of serving AI models at scale. On October 20th, the project reached its 1.0 release, adding operational controls for running distributed inference after deployment, including multi-tenant traffic priorities, router failover and rolling upgrades.…
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