diff --git a/app_tests/benchmark_tests/llm/sglang_benchmarks/conftest.py b/app_tests/benchmark_tests/llm/sglang_benchmarks/conftest.py
index 27bfddfa2..1a2633b0e 100644
--- a/app_tests/benchmark_tests/llm/sglang_benchmarks/conftest.py
+++ b/app_tests/benchmark_tests/llm/sglang_benchmarks/conftest.py
@@ -68,35 +68,35 @@ def write_config(request, pre_process_model):
     batch_sizes = request.param["batch_sizes"]
     prefix_sharing_algorithm = request.param["prefix_sharing_algorithm"]
 
-    logger.info("Writing config file..." + start_log_group("Writing config file"))
-
+    # Construct the new config filename
     config_path = (
         pre_process_model
         / f"{'_'.join(str(bs) for bs in batch_sizes)}_{prefix_sharing_algorithm}.json"
     )
 
-    config = {
-        "module_name": "module",
-        "module_abi_version": 1,
-        "max_seq_len": 131072,
-        "attn_head_count": 8,
-        "attn_head_dim": 128,
-        "prefill_batch_sizes": batch_sizes,
-        "decode_batch_sizes": batch_sizes,
-        "transformer_block_count": 32,
-        "paged_kv_cache": {
-            "block_seq_stride": 16,
-            "device_block_count": 256,
-            "prefix_sharing_algorithm": prefix_sharing_algorithm,
-        },
-    }
+    # Read the base config file
+    base_config_path = pre_process_model / "config.json"
+    with open(base_config_path, "r") as f:
+        config = json.load(f)
+
+    # Override specific fields
+    config.update(
+        {
+            "prefill_batch_sizes": batch_sizes,
+            "decode_batch_sizes": batch_sizes,
+            "paged_kv_cache": {
+                **config.get(
+                    "paged_kv_cache", {}
+                ),  # Preserve other paged_kv_cache settings
+                "prefix_sharing_algorithm": prefix_sharing_algorithm,
+            },
+        }
+    )
 
     logger.info(f"Saving edited config to: {config_path}\n")
     logger.info(f"Config: {json.dumps(config, indent=2)}")
     with open(config_path, "w") as f:
         json.dump(config, f)
-
-    logger.info("Config file successfully written" + end_log_group())
     yield config_path
 
 
diff --git a/app_tests/integration_tests/llm/sglang/conftest.py b/app_tests/integration_tests/llm/sglang/conftest.py
index 8543708da..cc79fc365 100644
--- a/app_tests/integration_tests/llm/sglang/conftest.py
+++ b/app_tests/integration_tests/llm/sglang/conftest.py
@@ -64,21 +64,6 @@ def pre_process_model(request, tmp_path_factory):
         device_settings,
     )
 
-    config = {
-        "module_name": "module",
-        "module_abi_version": 1,
-        "max_seq_len": 131072,
-        "attn_head_count": 8,
-        "attn_head_dim": 128,
-        "prefill_batch_sizes": [1, 4],
-        "decode_batch_sizes": [1, 4],
-        "transformer_block_count": 32,
-        "paged_kv_cache": {"block_seq_stride": 16, "device_block_count": 256},
-    }
-    config_path = tmp_dir / "config.json"
-    with open(config_path, "w") as f:
-        json.dump(config, f)
-
     return tmp_dir
 
 
diff --git a/app_tests/integration_tests/llm/shortfin/conftest.py b/app_tests/integration_tests/llm/shortfin/conftest.py
index 42e541506..55c9e8bdc 100644
--- a/app_tests/integration_tests/llm/shortfin/conftest.py
+++ b/app_tests/integration_tests/llm/shortfin/conftest.py
@@ -87,26 +87,30 @@ def write_config(request, model_test_dir):
     batch_sizes = request.param["batch_sizes"]
     prefix_sharing_algorithm = request.param["prefix_sharing_algorithm"]
 
+    # Construct the new config filename
     config_path = (
         model_test_dir
         / f"{'_'.join(str(bs) for bs in batch_sizes)}_{prefix_sharing_algorithm}.json"
     )
 
-    config = {
-        "module_name": "module",
-        "module_abi_version": 1,
-        "max_seq_len": 2048,
-        "attn_head_count": 32,
-        "attn_head_dim": 100,
-        "prefill_batch_sizes": batch_sizes,
-        "decode_batch_sizes": batch_sizes,
-        "transformer_block_count": 26,
-        "paged_kv_cache": {
-            "block_seq_stride": 16,
-            "device_block_count": 256,
-            "prefix_sharing_algorithm": prefix_sharing_algorithm,
-        },
-    }
+    # Read the base config file
+    base_config_path = model_test_dir / "config.json"
+    with open(base_config_path, "r") as f:
+        config = json.load(f)
+
+    # Override specific fields
+    config.update(
+        {
+            "prefill_batch_sizes": batch_sizes,
+            "decode_batch_sizes": batch_sizes,
+            "paged_kv_cache": {
+                **config.get(
+                    "paged_kv_cache", {}
+                ),  # Preserve other paged_kv_cache settings
+                "prefix_sharing_algorithm": prefix_sharing_algorithm,
+            },
+        }
+    )
     logger.info(f"Saving edited config to: {config_path}\n")
     logger.info(f"Config: {json.dumps(config, indent=2)}")
     with open(config_path, "w") as f:
diff --git a/sharktank/sharktank/examples/export_paged_llm_v1.py b/sharktank/sharktank/examples/export_paged_llm_v1.py
index 6dd9785c3..900c1a9ae 100644
--- a/sharktank/sharktank/examples/export_paged_llm_v1.py
+++ b/sharktank/sharktank/examples/export_paged_llm_v1.py
@@ -7,6 +7,7 @@
 """Export support for the PagedLLMV1 protocol of models."""
 
 import json
+from typing import Any, Dict
 import torch
 
 from iree.turbine.aot import *
@@ -86,17 +87,29 @@ def main():
     else:
         model = PagedLlamaModelV1(dataset.root_theta, llama_config)
 
-    def generate_params_json(hp, prefill_bs: list[int], decode_bs: list[int]):
+    def generate_params_json(
+        hp: LlamaHParams, prefill_bs: list[int], decode_bs: list[int]
+    ) -> Dict[str, Any]:
+        """
+        Generate config.json for shortfin.
+
+
+        For shortfin, we only write attention_head_count_kv because that's all shortfin needs.
+        Note that this is different from hp.attn_head_count when grouped attention shares kvcache between heads.
+        """
         return {
             "module_name": "module",
             "module_abi_version": 1,
             "max_seq_len": hp.context_length,
-            "attn_head_count": hp.attention_head_count,
             "attn_head_dim": hp.attn_head_dim,
             "prefill_batch_sizes": prefill_bs,
             "decode_batch_sizes": decode_bs,
             "transformer_block_count": hp.block_count,
-            "block_seq_stride": llama_config.block_seq_stride,
+            "paged_kv_cache": {
+                "attention_head_count_kv": hp.attention_head_count_kv,
+                "block_seq_stride": llama_config.block_seq_stride,
+                "device_block_count": 256,  # so that this makes its way into the config file & can be edited.
+            },
         }
 
     # Unrolling cache updates by batch row makes dynamo sad without an
diff --git a/shortfin/python/shortfin_apps/llm/components/config_struct.py b/shortfin/python/shortfin_apps/llm/components/config_struct.py
index 7caed5d07..8fefa0a12 100644
--- a/shortfin/python/shortfin_apps/llm/components/config_struct.py
+++ b/shortfin/python/shortfin_apps/llm/components/config_struct.py
@@ -11,20 +11,20 @@
 In a typical transformer model, the KV cache is organized similar to (mapped to
 our parameter names below):
     k = tensor.empty(transformer_block_count, batch_size, seq,
-                    attn_head_count, attn_head_dim)
+                    attn_head_count_kv, attn_head_dim)
     v = ...
 
 For context, a popular model has parameters of:
     attn_dtype_size = 2  # (fp16)
     max_seq_len = 2048
     transformer_block_count = 32
-    attn_head_count = 32
+    attn_head_count_kv = 32
     attn_head_dim = 128   # (dim / head_count)
 
 If paging, then we primarily care about the organization of a single block, where
 a block represents a single position in the sequence for a single item in the batch.
 Therefore, it will be organized like:
-    block = torch.empty(transformer_block_count, 2, attn_head_count, attn_head_dim)
+    block = torch.empty(transformer_block_count, 2, attn_head_count_kv, attn_head_dim)
 
 In this scenario, we declare that one block holds the KV cache for all transformer
 block layers because it reduces the accounting. As such, for the above example,
@@ -80,10 +80,15 @@ def _decode_dtype(name: str) -> sfnp.DType:
 class PagedKVCacheParams:
     """Parameters for the paged KV cache."""
 
-    # Position stride per attention block
+    # Tokens per page.
     block_seq_stride: int
 
+    # Number of attention heads per block. This can be different from the model's
+    # attention head count due to sharing.
+    attention_head_count_kv: int
+
     # Size of the cache on each device.
+    # Default: 256
     device_block_count: int
 
     prefix_sharing_algorithm: str = "none"  # currently supporting none and trie
@@ -92,19 +97,23 @@ class PagedKVCacheParams:
 @dataclass_json(undefined=Undefined.RAISE)
 @dataclass
 class ModelParams:
-    """Parameters for a specific compiled model, sufficient to do cache planning and
-    invocations."""
+    """
+    Parameters for a specific compiled model, sufficient to do cache planning and
+    invocations.
+
+    Compatibility should be maintained with function generate_params_json in
+
+    sharktank/sharktank/examples/export_paged_llm_v1.py
+    """
 
     # Maximum length of a sequence including prompt and output.
     max_seq_len: int
 
-    # Number of transformer blocks.
+    # Number of transformer layers (aka attention blocks / transformer blocks).
     transformer_block_count: int
 
-    # Number of attention heads per block.
-    attn_head_count: int
-
-    # Dimensionality of each attention head
+    # Dimensionality of each attention head. This is the dimensionality of the
+    # key and value vectors. AKA rope_dimension_count from the GGUF props.
     attn_head_dim: int
 
     # Batch sizes that the prefill stage is compiled for. These are expected to be
@@ -159,7 +168,7 @@ def paged_kv_unit_size_elements(self) -> int:
         size = 1
         size *= self.transformer_block_count
         size *= 2  # K and V cache line
-        size *= self.attn_head_count
+        size *= self.paged_kv_cache.attention_head_count_kv
         size *= self.attn_head_dim
         return size