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Research questionHow can enterprises consolidate heterogeneous internal workloads onto one self-hosted LLM under data-residency constraints?Enterprises increasingly retain older models while adopting newer ones, expanding the serving fleet and fragmenting limited GPU capacity. Data-residency requirements make consolidation difficult because a single self-hosted model must cover substantially different request types without unacceptable quality gaps.
AI
Evaluation & Benchmarks
Inference Optimization
LLM Pretraining & Post-training
Technology
Latest papersRecent research connected to this question, newest first.From Production Traffic to Post-Training: Building a Self-Hosted LLM That Covers the Corporate Request MixThe source reports a self-hosted model serving traffic from more than 200 internal applications. Evidence includes production-stratified offline benchmarks, deterministic verifiers or calibrated LLM judges, an in-house Arena, and deployment covering 50% of platform traffic, or 116 million requests per month; reported quality results are for non-reasoning mode.research paper · Sep 1, 2026
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