Update README.md

README.md

@@ -72,7 +72,41 @@ Stay tuned!
 
 Nandi-Mini-500M introduces several efficiency-focused architectural optimizations designed for compact yet capable language models.
 
-#### Shared KV (Shared Key-Value Vectors)
+
+### Shared KV KV-Cache Memory Comparison
+
+The following comparison illustrates the KV-cache memory reduction enabled by Shared KV mode.
+
+```python
+import matplotlib.pyplot as plt
+
+modes = ["Vanilla KV", "Shared KV"]
+memory = [100, 50]
+
+plt.figure(figsize=(5,4))
+bars = plt.bar(modes, memory)
+
+plt.ylabel("Relative KV Cache Memory")
+plt.title("KV Cache Memory Usage")
+
+for bar, val in zip(bars, memory):
+    plt.text(
+        bar.get_x() + bar.get_width()/2,
+        val + 2,
+        f"{val}%",
+        ha='center'
+    )
+
+plt.ylim(0, 120)
+plt.show()
+```
+
+Expected result:
+
+- Vanilla KV → 100% KV-cache memory
+- Shared KV → ~50% KV-cache memory
+
+Shared KV trades a small increase in compute overhead for significantly lower memory usage, since RoPE and Key normalization are applied dynamically during attention computation.
 
 Shared KV is one of the core architectural ideas explored in Nandi-Mini. Instead of storing separate Key and Value vectors, both share the same underlying representation, while a lightweight Key normalization step is applied specifically for attention computation.