Cut GPU inference cold start from 8 minutes to less than a minute
We instrumented the full path from pod creation to first inference response on a GPU node running a 70B-class model. The post Cut GPU inference cold…
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We instrumented the full path from pod creation to first inference response on a GPU node running a 70B-class model. The post Cut GPU inference cold…
Every token has a price. The problem is that most AI systems don’t reveal the bill until they reach production. The post Why your AI pipeline costs…
Meta released Muse Glimmer on Monday, a 30-billion-parameter open-weight model distilled from Muse Spark and licensed under Apache 2.0. The The post…
The seemingly unquenchable thirst of the AI data ingestion pipeline spans language, numerical, and tabular data in the first instance, while Th…
Nothing matches the dread of checking a perfectly green observability dashboard with sub-100ms latency, right before an enterprise client emails The…
This article explains why traditional CI/CD gates are not enough for production AI systems. I share a practical release-gating approach The post Why…
Recently, a crucial RAG pipeline used by one of our corporate clients began hallucinating about financial numbers without notifying us The post Your…
At NetEase Games, we learned a hard lesson about large language model (LLM) inference in production: elastic compute is only The post How NetEase Ga…