vLLM makes Model Runner V2 default

The AI inference race has shifted from brute matrix multiplication to control plane engineering and cache management. Serving efficiency is now governed by runner architecture, KV cache placement, and safe state routing rather than raw compute alone. Defaulting to Model Runner V2 turns vLLM from a fast server into a resilient control plane for heterogeneous hardware and multimodal payloads. SGLang focus on Rust routing and SGLang-Omni proves production stacks require specialized control planes for low latency and real-time modalities. Ollama embedding directly into ChatGPT Desktop shows local models are infiltrating everyday developer workflows without forcing frontend switches. Rapid backend tuning across llama.cpp and FlashInfer highlights that kernel-level specialization for Blackwell and ROCm is outpacing high-level framework releases.