agent_rle.cuh (1072 lines) complete design: BlockDiscontinuity for
segment detection + streaming_context for cross-tile state + ScatterDirect
for compact output. Maps to reasoning/content segment detection in
streaming SSE responses.
adjacent_difference maps to streaming delta_text computation.
tuning_select_if.cuh (2729 lines) complete design analysis:
- 3-level dispatch: compute_capability → sm_tuning → benchmark params
- Per-type/per-mode/per-hardware specialization tables
- Every param from real benchmark (annotated with 4 speedup ratios)
- Fallback to conservative default when no tuning match
Maps to our serving layer:
- Request type dispatch (tool/reasoning/basic) = compute_capability
- max_tokens cap by type = threads_per_block/items_per_thread
- Sub168 log data = benchmark annotations
- default_policy = conservative fallback
No code changes needed — current serving_chat.py already implements
this 3-level dispatch pattern with Sub168 benchmark-derived params.
Records CCCL source → base modification mappings from each loop iteration.
Strategy: serving-layer-only patches + env var tuning, never touch model layer.
