diff --git a/ml/backend/ggml/ggml.go b/ml/backend/ggml/ggml.go
index 1ba079838d..2821ad119e 100644
--- a/ml/backend/ggml/ggml.go
+++ b/ml/backend/ggml/ggml.go
@@ -915,6 +915,8 @@ func (t *Tensor) RMSNorm(ctx ml.Context, w ml.Tensor, eps float32) ml.Tensor {
 func (t *Tensor) Pad(ctx ml.Context, shape ...int) ml.Tensor {
 	if len(shape) != 4 {
 		panic("expected 4 dimensions")
+	} else if shape[3] != 0 {
+		panic("cuda does not support 4d tensors")
 	}
 
 	return &Tensor{
diff --git a/model/models/mllama/model.go b/model/models/mllama/model.go
index 93b443ef1e..4d5bdd4a18 100644
--- a/model/models/mllama/model.go
+++ b/model/models/mllama/model.go
@@ -3,6 +3,7 @@ package mllama
 import (
 	"bytes"
 	"image"
+	"slices"
 
 	"github.com/ollama/ollama/fs"
 	"github.com/ollama/ollama/kvcache"
@@ -73,13 +74,17 @@ func (m *Model) EncodeMultimodal(ctx ml.Context, multimodalData []byte) (any, er
 		return nil, err
 	}
 
-	pixelValues, err := ctx.Input().FromFloatSlice(f32s, m.imageSize, m.imageSize, m.numChannels, ratio.numTiles())
+	if ratio.numTiles() < m.maxNumTiles {
+		// Pad tiles to maxNumTiles
+		f32s = slices.Grow(f32s, m.imageSize*m.imageSize*m.numChannels*m.maxNumTiles)
+		f32s = f32s[:m.imageSize*m.imageSize*m.numChannels*m.maxNumTiles]
+	}
+
+	pixelValues, err := ctx.Input().FromFloatSlice(f32s, m.imageSize, m.imageSize, m.numChannels, m.maxNumTiles)
 	if err != nil {
 		return nil, err
 	}
 
-	pixelValues = pixelValues.Pad(ctx, 0, 0, 0, m.ImageProcessor.maxNumTiles-ratio.numTiles())
-
 	aspectRatio, err := ctx.Input().FromIntSlice([]int32{int32(ratio.rank)}, 1)
 	if err != nil {
 		return nil, err