Marvin G6 | Engineering, AI & Automation

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vLLM

A collection of 2 issues

The Scaling Plateau Is Here: Why Post-Training and Inference Optimization Win in 2026

LLM scaling returns are shrinking. Learn how LoRA, QLoRA, knowledge distillation, and vLLM inference techniques can cut serving costs by up to 80%.

The Hidden Infrastructure Tax: Running GLM-5.2, Kimi K2.7, and Other Frontier Open-Weight Models in Production

Self-hosting GLM-5.2 or Kimi K2.7? Learn the real infrastructure costs: KV cache sizing, GPU networking, serving frameworks, and a framework for deciding when to self-host.