diff --git a/qdrant-landing/content/blog/qdrant-1.16.x.md b/qdrant-landing/content/blog/qdrant-1.16.x.md
index 715f35fd3..dc1dfd7e5 100644
--- a/qdrant-landing/content/blog/qdrant-1.16.x.md
+++ b/qdrant-landing/content/blog/qdrant-1.16.x.md
@@ -105,6 +105,32 @@ However, disk-based storage has a property we can exploit to reduce the number o
That is, unless we can duplicate the data associated with each node. This is now possible with a new feature in Qdrant version 1.16: [inline storage](documentation/guides/optimize/#inline-storage-in-hnsw-index), storing vector data directly inside the HNSW nodes. This offers faster read access, at the cost of additional storage space.
+
+
+
+ Storage layout without inline storage. Full vectors, quantized vectors, and HNSW graph are stored separately.
+ The HNSW graph nodes contain only neighbor IDs.
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+
+
+How does it work?
+During a single iteration of HNSW search, the neighbors of the current node are scored using quantized vectors in order to add them to the search queue.
+Without inline storage, this results in 1+hnsw\_m disk reads.
+
+
+
+
+ A single HNSW graph node with inline storage enabled.
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+
+
+With the inline storage enabled, the quantized vectors are directly embedded into the HNSW graph nodes, alongside neighbor IDs.
+During a single search iteration, both neighbor IDs and their quantized vectors are read from a few consecutive pages, in a single disk read.
+
+Moreover, an original non-quantized vector is also embedded into the same graph node.
+The original vector is used to perform an implicit rescoring during the search,
+eliminating the separate rescore step which is usually performed after the search.
+
Let's do some napkin math:
- An HNSW graph has `M0` = `M` * 2 = 32 connections per node (by default)
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