diff --git a/benchmarks/python/sdpa_vector_bench.py b/benchmarks/python/sdpa_vector_bench.py
index 936514644b..ac58572cf6 100644
--- a/benchmarks/python/sdpa_vector_bench.py
+++ b/benchmarks/python/sdpa_vector_bench.py
@@ -3,7 +3,7 @@
 from mlx.utils import tree_map
 from time_utils import time_fn
 
-L = 16384
+L = 65536
 H = 32
 H_k = H // 4
 D = 128
@@ -27,13 +27,13 @@ def sdpa(q, k, v):
     return mx.fast.scaled_dot_product_attention(q, k, v, scale=1.0, mask=None)
 
 
-def quant_sdpa(q, k, v):
+def quant_sdpa(q, k, v, bits=4):
     return mx.fast.quantized_scaled_dot_product_attention(
-        q, *k, *v, scale=1.0, mask=None, bits=8
+        q, *k, *v, scale=1.0, mask=None, bits=bits
     )
 
 
-def quant_attention(q, k, v):
+def quant_attention(q, k, v, bits=4):
     B, Hq, L, D = q.shape
     Hk = k[0].shape[1]
 
@@ -41,10 +41,10 @@ def quant_attention(q, k, v):
     k = tree_map(lambda x: mx.expand_dims(x, axis=2), k)
     v = tree_map(lambda x: mx.expand_dims(x, axis=2), v)
 
-    scores = mx.quantized_matmul(q, *k, transpose=True)
+    scores = mx.quantized_matmul(q, *k, transpose=True, bits=bits)
     scores = mx.softmax(scores, axis=-1)
 
-    out = mx.quantized_matmul(scores, *v, transpose=False)
+    out = mx.quantized_matmul(scores, *v, transpose=False, bits=bits)
     out = out.reshape((B, Hq, L, D))
     return out
 
@@ -57,12 +57,12 @@ def time_self_attention_sdpa(q, k, v):
     time_fn(sdpa, q, k, v)
 
 
-def time_self_attention_quant_sdpa(q, k, v):
-    time_fn(quant_sdpa, q, k, v)
+def time_self_attention_quant_sdpa(q, k, v, bits=4):
+    time_fn(quant_sdpa, q, k, v, bits)
 
 
-def time_self_attention_quant_primitives(q, k, v):
-    time_fn(quant_attention, q, k, v)
+def time_self_attention_quant_primitives(q, k, v, bits=4):
+    time_fn(quant_attention, q, k, v, bits)
 
 
 if __name__ == "__main__":
@@ -72,11 +72,12 @@ def time_self_attention_quant_primitives(q, k, v):
     v = mx.random.uniform(shape=(1, H_k, L, D))
     mx.eval(q, k, v)
 
-    k_quant = mx.quantize(k)
-    v_quant = mx.quantize(v)
+    bits = 4
+    k_quant = mx.quantize(k, bits=bits)
+    v_quant = mx.quantize(v, bits=bits)
     mx.eval(k_quant, v_quant)
 
     time_self_attention_sdpa(q, k, v)
-    time_self_attention_quant_sdpa(q, k_quant, v_quant)
+    time_self_attention_quant_sdpa(q, k_quant, v_quant, bits)
     time_self_attention_primitives(q, k, v)
-    time_self_attention_quant_primitives(q, k_quant, v_quant)
+    time_self_attention_quant_primitives(q, k_quant, v_quant, bits)
diff --git a/mlx/backend/metal/scaled_dot_product_attention.cpp b/mlx/backend/metal/scaled_dot_product_attention.cpp
index c66b33a002..4965dacb4f 100644
--- a/mlx/backend/metal/scaled_dot_product_attention.cpp
+++ b/mlx/backend/metal/scaled_dot_product_attention.cpp
@@ -306,7 +306,7 @@ void quant_sdpa_vector(
 void ScaledDotProductAttention::eval_gpu(
     const std::vector<array>& inputs,
     array& out) {
-assert(inputs.size() == 3);
+  assert(inputs.size() == 3);
 
   auto& s = stream();
   auto& d = metal::device(s.device);
@@ -353,7 +353,7 @@ assert(inputs.size() == 3);
 
   // We are in vector mode ie single query
   if (q_pre.shape(2) == 1) {
-      auto q = copy_unless(is_contiguous, q_pre);
+    auto q = copy_unless(is_contiguous, q_pre);
 
     // Donate the query if possible
     if (q.is_donatable()) {
@@ -380,9 +380,20 @@ assert(inputs.size() == 3);
       auto v_biases = copy_unless(is_contiguous_except_seq_len, v_biases_pre);
 
       quant_sdpa_vector(
-          s, d, q, k, k_scales, k_biases, v, v_scales, v_biases, o, scale_, group_size_, bits_);
-    }
-    else {
+          s,
+          d,
+          q,
+          k,
+          k_scales,
+          k_biases,
+          v,
+          v_scales,
+          v_biases,
+          o,
+          scale_,
+          group_size_,
+          bits_);
+    } else {
       auto& k_pre = inputs[1];
       auto& v_pre = inputs[2];
 
@@ -396,15 +407,10 @@ assert(inputs.size() == 3);
       } else {
         sdpa_vector(s, d, q, k, v, o, scale_);
       }
-
     }
-
-    // We route to the 2 pass fused attention if
-    // - The device is large and the sequence length long
-    // - The sequence length is even longer and we have gqa
   }
 
-  // Non-quantized
+  // Full attention mode
   else {
     auto& v_pre = inputs[2];
 
@@ -430,30 +436,7 @@ assert(inputs.size() == 3);
         {str_oB, str_oH, str_oL, str_oD},
         flags);
 
-    // We are in vector mode ie single query
-    if (q_pre.shape(2) == 1) {
-      auto q = copy_unless(is_contiguous, q_pre);
-      auto k = copy_unless(is_contiguous_except_seq_len, k_pre);
-      auto v = copy_unless(is_contiguous_except_seq_len, v_pre);
-
-      // Donate the query if possible
-      if (q.is_donatable()) {
-        o.move_shared_buffer(q);
-      } else {
-        o.set_data(allocator::malloc_or_wait(o.nbytes()));
-      }
-
-      sdpa_vector(s, d, q, k, v, o, scale_);
-    }
-    // Full attention mode
-    else {
-      auto q = copy_unless(is_matrix_contiguous, q_pre);
-      auto k = copy_unless(is_matrix_contiguous, k_pre);
-      auto v = copy_unless(is_matrix_contiguous, v_pre);
-      o.set_data(allocator::malloc_or_wait(o.nbytes()));
-
-      sdpa_full_self_attention_metal(s, d, q, k, v, scale_, o);
-    }
+    sdpa_full_self_attention_metal(s, d, q, k, v, scale_, o);
   }
 
   d.add_temporaries(std::move(copies), s.index);