--- a/ml/examples/hyperopt_mamba2_demo.rs
+++ b/ml/examples/hyperopt_mamba2_demo.rs
@@ -66,8 +66,9 @@ struct Args {
     batch_size_min: usize,

-    /// Maximum batch size for GPU memory constraints (default: 96 for RTX A4000 16GB)
-    /// Examples: RTX 3050 Ti 4GB = 32, RTX A4000 16GB = 96, RTX 4090 24GB = 256
-    #[arg(long, default_value = "96")]
+    /// Maximum batch size for GPU memory constraints (default: 180 for RTX A4000 16GB)
+    /// Validated safe limits based on VRAM analysis (AGENT_R3_A5_VRAM_ANALYSIS.md):
+    /// Examples: RTX 3050 Ti 4GB = 32, RTX A4000 16GB = 180, RTX 4090 24GB = 256
+    #[arg(long, default_value = "180")]
     batch_size_max: usize,
 }

--- a/ml/src/hyperopt/adapters/mamba2.rs
+++ b/ml/src/hyperopt/adapters/mamba2.rs
@@ -115,7 +115,8 @@ impl ParameterSpace for Mamba2Params {
     fn continuous_bounds() -> Vec<(f64, f64)> {
         vec![
             (1e-5_f64.ln(), 1e-2_f64.ln()), // learning_rate (log scale)
-            (4.0, 256.0),                    // batch_size (linear) - wide bounds, clamped by trainer config
+            (4.0, 180.0),                    // batch_size (linear) - validated safe for 16GB GPU
+                                             // See AGENT_R3_A5_VRAM_ANALYSIS.md for derivation
             (0.0, 0.5),                      // dropout (linear)
             (1e-6_f64.ln(), 1e-2_f64.ln()), // weight_decay (log scale)
                 (0.5_f64.ln(), 5.0_f64.ln()),   // grad_clip (log scale)