diff --git a/sharktank/sharktank/examples/export_paged_llm_v1.py b/sharktank/sharktank/examples/export_paged_llm_v1.py
index 900c1a9ae..43242e0c0 100644
--- a/sharktank/sharktank/examples/export_paged_llm_v1.py
+++ b/sharktank/sharktank/examples/export_paged_llm_v1.py
@@ -74,7 +74,7 @@ def main():
         tensor_parallelism_size=tensor_parallelism_size,
         use_hf=False,
         static_tables=False,  # Rely on the compiler for hoisting tables.
-        kv_cache_type="direct" if args.bs == [1] else "paged",
+        kv_cache_type="paged" if args.bs == [1] else "paged",
         attention_kernel=args.attention_kernel,
     )
     llama_config.fake_quant = args.fake_quant
diff --git a/sharktank/sharktank/layers/configs/llm_configs.py b/sharktank/sharktank/layers/configs/llm_configs.py
index 8a443e6ca..6cf79402e 100644
--- a/sharktank/sharktank/layers/configs/llm_configs.py
+++ b/sharktank/sharktank/layers/configs/llm_configs.py
@@ -144,7 +144,7 @@ class LlamaModelConfig:
 
     # Block sequence stride for a paged KV cache. This must divide evenly
     # into the context length.
-    block_seq_stride: int = 16
+    block_seq_stride: int = 32
 
     # Either "paged" or "direct".
     kv_cache_type: str = "paged"
@@ -167,7 +167,7 @@ class LlamaModelConfig:
     tensor_parallelism_size: int = 1
 
     # Which attention kernel to use.
-    attention_kernel: str = "decomposed"
+    attention_kernel: str = "torch"
 
     # Indicates if running with HuggingFace implementation and ensures
     # numerical equivalency to HuggingFace's LLaMa if true (by modifying
diff --git a/sharktank/sharktank/layers/paged_llama_attention_block.py b/sharktank/sharktank/layers/paged_llama_attention_block.py
index 22647bf49..6bd33c93f 100644
--- a/sharktank/sharktank/layers/paged_llama_attention_block.py
+++ b/sharktank/sharktank/layers/paged_llama_attention_block.py
@@ -216,14 +216,12 @@ def repeat_kv(x: torch.Tensor) -> torch.Tensor:
                 attn_weights, values
             )  # (bs, heads, slen, head_dim)
         else:
-            is_causal = True
-            attention_mask = None
             attn_output = ops.scaled_dot_product_attention(
                 q=xq,  # [bs, ..., sl, dim]
                 k=keys,  # [bs, ..., sl, dim]
                 v=values,  # [bs, ..., sl, dim]
                 a=attention_mask,  # [bs, ..., sl, sl]
-                is_causal=is_causal,  # assumes causal masking when true
+                is_causal=False,  # assumes causal masking when true
                 scale=None,  # defaults to 1/sqrt(dim)
             )
 
diff --git a/sharktank/sharktank/utils/cli.py b/sharktank/sharktank/utils/cli.py
index 99917c2d3..9fefeb66f 100644
--- a/sharktank/sharktank/utils/cli.py
+++ b/sharktank/sharktank/utils/cli.py
@@ -66,7 +66,7 @@ def add_model_options(parser: argparse.ArgumentParser):
     parser.add_argument(
         "--attention-kernel",
         type=str,
-        default="decomposed",
+        default="torch",
         choices=["decomposed", "torch"],
     )
     parser.add_argument(