Building a full SaaS product on Qwen 3.8 27B with vLLM

This person builds a full-scale SaaS product using Qwen 3.8 27B, describing the key to making it work as 'harness-maxxing' — close attention to context management and feedback loops rather than the model alone. On their AMD cards, they found llama.cpp recomputed the prefill on every subagent return, which killed throughput; switching to vLLM's paged attention kept the GPU doing useful work continuously once tuned, to the point they now consider llama.cpp comparatively toy-like for this workload.

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