diff --git a/training/dashboard.py b/training/dashboard.py
index 9b92d32..b4c795c 100644
--- a/training/dashboard.py
+++ b/training/dashboard.py
@@ -162,12 +162,17 @@ def generate_text(W, max_tokens=64, temperature=0.8):
             freq = 1.0 / (10000.0 ** (2.0 * i / HD))
             freqs[pos, i] = pos * freq
 
+    # KV cache: per-layer, per-head arrays
+    k_cache = [[np.zeros((0, HD), dtype=np.float32) for _ in range(HEADS)] for _ in range(NLAYERS)]
+    v_cache = [[np.zeros((0, HD), dtype=np.float32) for _ in range(HEADS)] for _ in range(NLAYERS)]
+
     for step in range(max_tokens):
         seq_len = len(tokens)
         if seq_len > SEQ:
             break
 
         x = W['embed'][tokens[-1]].copy()
+        pos = seq_len - 1
 
         for L in range(NLAYERS):
             # RMSNorm + QKV
@@ -177,7 +182,6 @@ def generate_text(W, max_tokens=64, temperature=0.8):
             v = W[f'Wv{L}'] @ xn
 
             # RoPE
-            pos = seq_len - 1
             for h in range(HEADS):
                 for i in range(HD // 2):
                     freq = freqs[pos, i]
@@ -189,14 +193,18 @@ def generate_text(W, max_tokens=64, temperature=0.8):
                     k[h * HD + 2 * i] = ki * cos_v - ki1 * sin_v
                     k[h * HD + 2 * i + 1] = ki * sin_v + ki1 * cos_v
 
-            # Attention (single token)
+            # Append to KV cache and compute attention
             o = np.zeros(DIM, dtype=np.float32)
             for h in range(HEADS):
                 qh = q[h * HD:(h + 1) * HD]
-                kh = k[h * HD:(h + 1) * HD]
-                vh = v[h * HD:(h + 1) * HD]
-                score = np.dot(qh, kh) / math.sqrt(HD)
-                o[h * HD:(h + 1) * HD] = vh
+                kh = k[h * HD:(h + 1) * HD].reshape(1, HD)
+                vh = v[h * HD:(h + 1) * HD].reshape(1, HD)
+                k_cache[L][h] = np.vstack([k_cache[L][h], kh])
+                v_cache[L][h] = np.vstack([v_cache[L][h], vh])
+                # scores: (1, HD) @ (HD, seq_len) -> (seq_len,)
+                scores = k_cache[L][h] @ qh / math.sqrt(HD)
+                attn = softmax(scores)
+                o[h * HD:(h + 1) * HD] = attn @ v_cache[L][h]
 
             # Residual + output projection
             x2 = x + W[f'Wo{L}'] @ o