diff --git a/docs/CCCL_REDUCE_ARCHITECTURE_NOTES.md b/docs/CCCL_REDUCE_ARCHITECTURE_NOTES.md
index c0ac8e8a..7fa8c69d 100644
--- a/docs/CCCL_REDUCE_ARCHITECTURE_NOTES.md
+++ b/docs/CCCL_REDUCE_ARCHITECTURE_NOTES.md
@@ -352,3 +352,61 @@ Benchmark result (bif=8, alg=1, pref=2, tpb=256, unrl=1, vsp2=1):
   tpb=256 = matches CCCL default
   vsp2=1 → vec_size parameter (powers of 2, so vsp2=1 means vec_size=2)
   speedups: 1.203199 1.058919 1.019168 (fp16, 1M/16M/64M)
+
+---
+
+## GridEvenShare Work Distribution (grid_even_share.cuh)
+
+> Added: 2026-08-04
+
+### Two strategies: RAKE vs STRIP_MINE
+
+**RAKE** (scan uses this): consecutive tiles per block
+  block k gets tiles [k*avg .. k*avg + avg-1]
+  block_stride = TILE_ITEMS (contiguous, no gaps)
+
+**STRIP_MINE** (reduce uses this): interleaved tiles
+  block k gets tiles k, k+grid_size, k+2*grid_size, ...
+  block_stride = grid_size * TILE_ITEMS (strided)
+
+### Concrete numbers for BI-V100 attention score reduce
+
+tile_items = 512 * 24 = 12288 (bi100_plus_float32_o4)
+max_grid = 2 * 16 * 5 = 160 (occupancy * SMs * subscription)
+
+| seq_len | total_tiles | grid_size | tiles/block | waves |
+|---------|-------------|-----------|-------------|-------|
+| 1K      | 1           | 1         | 1           | 1     |
+| 8K      | 1           | 1         | 1           | 1     |
+| 32K     | 3           | 3         | 1           | 1     |
+| 100K    | 9           | 9         | 1           | 1     |
+| 1M      | 82          | 82        | 1           | 3     |
+
+Even at 100K tokens, only 9 CTAs are needed → everything fits in one
+wave on 16 SMs. This means:
+
+1. Reduce tuning (items/threads) matters less than expected — there 
+   are so few tiles that the per-tile overhead dominates, not throughput.
+
+2. The V1/V2 choice in paged_attn.py matters MORE — V1 doesn't use
+   GridEvenShare at all, it's a single CTA iterating sequentially.
+   V2's partition-based approach enables parallel reduction.
+
+3. For short sequences (≤8K, 1 tile), SingleTile path triggers:
+   just 1 CTA, 1 kernel launch, no temp storage.
+
+### The "big shares" distribution
+
+GridEvenShare handles uneven tile counts:
+  avg_tiles_per_block = total_tiles / grid_size
+  big_shares = total_tiles % grid_size (blocks that get +1 tile)
+
+For 100K tokens with 9 tiles and 9 blocks: avg=1, big_shares=0.
+All blocks equal. No imbalance.
+
+For 1M tokens with 82 tiles and 82 blocks: avg=1, big_shares=0.
+Still perfectly balanced at 1 tile/block.
+
+Only when max_grid_size limits grid_size do we get imbalance:
+e.g., 200 tiles with max_grid=160 → avg=1, big_shares=40 (40 blocks
+get 2 tiles, 120 blocks get 1 tile).