diff --git a/README.md b/README.md
index a84d743..2087788 100644
--- a/README.md
+++ b/README.md
@@ -95,6 +95,17 @@ diagrams = ripser(data)['dgms']
 plot_diagrams(diagrams, show=True)
 ```
 
+You can also estimate the RAM requirements before running Ripser:
+
+```python
+import numpy as np
+from ripser import estimate_ripser_memory
+
+data = np.random.random((1000, 2))
+estimated_gb = estimate_ripser_memory(data, maxdim=2)
+print(f"Estimated RAM required: {estimated_gb:.2f} GB")
+```
+
 We also supply a Scikit-learn transformer style object if you would prefer to use that:
 
 ```
diff --git a/src/ripser/ripser.py b/src/ripser/ripser.py
index 5695ed4..eb7afc9 100644
--- a/src/ripser/ripser.py
+++ b/src/ripser/ripser.py
@@ -102,6 +102,60 @@ def get_greedy_perm(X, n_perm=None, distance_matrix=False, metric="euclidean"):
     return (idx_perm, lambdas, dperm2all)
 
 
+def estimate_ripser_memory(X, maxdim=1, distance_matrix=False):
+    """Estimate the RAM requirements for running Ripser.
+
+    Parameters
+    ----------
+    X : ndarray (n_samples, n_features)
+        A numpy array of either data or distance matrix.
+    
+    maxdim : int, optional (default=1)
+        Maximum homology dimension to be computed.
+    
+    distance_matrix : bool, optional (default=False)
+        Whether X is a distance matrix.
+
+    Returns
+    -------
+    float
+        Estimated RAM requirement in GB.
+
+    Notes
+    -----
+    This is a rough estimation based on:
+    1. Memory for distance matrix: O(n^2) where n is number of points
+    2. Memory for simplicial complex: O(n^(d+1)) where d is maxdim
+    3. Memory for persistence computation: O(n^3) worst case for matrix reduction
+    """
+    if distance_matrix:
+        n_points = X.shape[0]
+    else:
+        n_points = X.shape[0]
+    
+    # Base memory for distance matrix (8 bytes per float64)
+    distance_matrix_memory = (n_points * n_points * 8) / (1024**3)  # in GB
+    
+    # Memory for simplicial complex (worst case)
+    # For each dimension d, we need to store combinations of d+1 points
+    # Using scipy.special.comb for exact calculation would be more accurate
+    # but this is a rough upper bound
+    complex_memory = 0
+    for d in range(maxdim + 1):
+        # Approximate number of d-simplices (overestimate)
+        n_simplices = (n_points ** (d + 1)) / np.math.factorial(d + 1)
+        # Each simplex needs (d+1) integers for vertices + 1 float for filtration value
+        complex_memory += (n_simplices * ((d + 1) * 4 + 8)) / (1024**3)  # in GB
+    
+    # Memory for persistence computation (worst case)
+    # This is a rough estimate based on the boundary matrix
+    persistence_memory = (n_points**3 * 8) / (1024**3)  # in GB
+    
+    total_memory = distance_matrix_memory + complex_memory + persistence_memory
+    
+    return total_memory
+
+
 def ripser(
     X,
     maxdim=1,
@@ -651,4 +705,4 @@ def plot(
         )
 
 
-__all__ = ["Rips", "ripser", "lower_star_img"]
+__all__ = ["Rips", "ripser", "lower_star_img", "estimate_ripser_memory"]
diff --git a/test/test_memory_estimation.py b/test/test_memory_estimation.py
new file mode 100644
index 0000000..be54491
--- /dev/null
+++ b/test/test_memory_estimation.py
@@ -0,0 +1,39 @@
+import numpy as np
+from ripser import estimate_ripser_memory
+
+def test_memory_estimation():
+    # Create a small point cloud
+    n_points = 100
+    n_features = 3
+    X = np.random.rand(n_points, n_features)
+    
+    # Test memory estimation for different dimensions
+    mem_dim0 = estimate_ripser_memory(X, maxdim=0)
+    mem_dim1 = estimate_ripser_memory(X, maxdim=1)
+    mem_dim2 = estimate_ripser_memory(X, maxdim=2)
+    
+    # Memory should increase with dimension
+    assert mem_dim0 < mem_dim1 < mem_dim2
+    
+    # Test with distance matrix
+    D = np.random.rand(n_points, n_points)
+    D = (D + D.T) / 2  # Make it symmetric
+    np.fill_diagonal(D, 0)  # Zero diagonal
+    
+    mem_dist = estimate_ripser_memory(D, maxdim=1, distance_matrix=True)
+    assert mem_dist > 0
+
+def test_memory_scaling():
+    # Test how memory scales with number of points
+    n_points_small = 50
+    n_points_large = 100
+    n_features = 3
+    
+    X_small = np.random.rand(n_points_small, n_features)
+    X_large = np.random.rand(n_points_large, n_features)
+    
+    mem_small = estimate_ripser_memory(X_small, maxdim=1)
+    mem_large = estimate_ripser_memory(X_large, maxdim=1)
+    
+    # Memory should scale super-linearly with number of points
+    assert mem_large > 4 * mem_small  # Since complexity is O(n^3)
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