The two numbers that decide how much KV cache the share learner must leave an
engine were constants in the module that uses them. They are now
`vllm.kv_target_gb` and `vllm.kv_min_concurrency`, global or per model in its
`vllm` block, on both the Settings page and the model's vLLM section — because
the right answer differs per model by an order of magnitude: 2 GiB is correct
for a page-at-a-time OCR model and leaves an LLM serving a 128k window unable
to hold a single request. Setting the count to 0 opts out and sizes by GiB
alone, which is what an OCR engine wants.

The cluster page gets the same treatment the RunPod page just had: full-width
wrapper and the dense table class, across all nine of its tables. The widest is
ten columns, where the default 1rem of cell padding alone was 320px. Long
values (a node URL, a capability list) now wrap inside their cell instead of
widening the table past the window.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01S8LAP3ueNzQJtwzhgVbq66