diff --git a/cpp/metadata/model.cc b/cpp/metadata/model.cc
index 0cf0186336..f4ef42e848 100644
--- a/cpp/metadata/model.cc
+++ b/cpp/metadata/model.cc
@@ -87,7 +87,7 @@ ModelMetadata ModelMetadata::FromJSON(const picojson::object& metadata,
   if (metadata.count("attention_sink_size"))  // remove after sink is decoupled from model lib
     result.attention_sink_size = json::Lookup<int64_t>(metadata, "attention_sink_size");
   result.seqlen_padding_factor =
-      json::LookupOrDefault<int64_t>(metadata, "seqlen_padding_factor", 1);
+      json::LookupOrDefault<int64_t>(metadata, "seqlen_padding_factor", 16);
   result.tensor_parallel_shards = json::Lookup<int64_t>(metadata, "tensor_parallel_shards");
   result.pipeline_parallel_stages =
       json::LookupOrDefault<int64_t>(metadata, "pipeline_parallel_stages", 1);
diff --git a/cpp/serve/model.cc b/cpp/serve/model.cc
index 568501dd7e..6bf1c99c77 100644
--- a/cpp/serve/model.cc
+++ b/cpp/serve/model.cc
@@ -262,7 +262,7 @@ class ModelImpl : public ModelObj {
     // Begin forward with the sequence ids and new lengths.
     IntTuple seq_ids_tuple(seq_ids);
     IntTuple lengths_tuple(lengths.begin(), lengths.end());
-    ft_.kv_cache_begin_forward_func_(kv_cache_, seq_ids_tuple, lengths_tuple);
+    ft_.kv_cache_begin_forward_func_(kv_cache_, seq_ids_tuple, lengths_tuple, seqlen_padding_factor_);
 
     ObjectRef embeddings_dref_or_nd;
     if (!embeddings->IsInstance<DRefObj>()) {
@@ -372,7 +372,7 @@ class ModelImpl : public ModelObj {
     // Begin forward with the sequence ids and new lengths.
     IntTuple seq_ids_tuple(seq_ids);
     IntTuple lengths_tuple(lengths.begin(), lengths.end());
-    ft_.kv_cache_begin_forward_func_(kv_cache_, seq_ids_tuple, lengths_tuple);
+    ft_.kv_cache_begin_forward_func_(kv_cache_, seq_ids_tuple, lengths_tuple, seqlen_padding_factor_);
 
     // args: embeddings, logit_pos, kv_cache, params
     ObjectRef result{nullptr};
@@ -410,6 +410,12 @@ class ModelImpl : public ModelObj {
   Tensor BatchDecode(const ObjectRef& embeddings, const std::vector<int64_t>& seq_ids) final {
     NVTXScopedRange nvtx_scope("BatchDecode num_seqs=" + std::to_string(seq_ids.size()));
     int num_sequence = seq_ids.size();
+    
+    bool padded = num_sequence % seqlen_padding_factor_ != 0;
+    if (padded) {
+      num_sequence = (num_sequence + seqlen_padding_factor_ - 1) / seqlen_padding_factor_ *
+                     seqlen_padding_factor_;
+    }
 
     CHECK(ft_.decode_func_.defined())
         << "`decode_with_embed` function is not found in the model. Please make sure the model is "
@@ -422,7 +428,7 @@ class ModelImpl : public ModelObj {
     // Begin forward with the sequence ids and new lengths.
     IntTuple seq_ids_tuple(seq_ids);
     IntTuple lengths_tuple(std::vector<int64_t>(/*n=*/seq_ids.size(), /*v=*/1));
-    ft_.kv_cache_begin_forward_func_(kv_cache_, seq_ids_tuple, lengths_tuple);
+    ft_.kv_cache_begin_forward_func_(kv_cache_, seq_ids_tuple, lengths_tuple, seqlen_padding_factor_);
 
     ObjectRef embeddings_dref_or_nd;
     if (!embeddings->IsInstance<DRefObj>()) {
@@ -443,7 +449,7 @@ class ModelImpl : public ModelObj {
 
     // args: embeddings, kv_cache, params
     ObjectRef ret;
-    if (seq_ids.size() == 1) {
+    if (num_sequence == 1) {
       ret = ft_.single_batch_decode_func_(embeddings_dref_or_nd, kv_cache_, params_)
                 .cast<ObjectRef>();
     } else {
@@ -468,9 +474,14 @@ class ModelImpl : public ModelObj {
     }
     ft_.kv_cache_end_forward_func_(kv_cache_);
 
+    if (padded) {
+      // logits shape: (padded_batch, 1, vocab_size_)
+      // Slice to (real_batch, 1, vocab_size_)
+      logits = logits.CreateView({seq_ids.size(), 1, vocab_size_}, logits->dtype);
+    }
     // logits: (b, 1, v)
     ICHECK_EQ(logits->ndim, 3);
-    ICHECK_EQ(logits->shape[0], num_sequence);
+    ICHECK_EQ(logits->shape[0], seq_ids.size());
     ICHECK_EQ(logits->shape[1], 1);
     return logits;
   }
@@ -501,7 +512,7 @@ class ModelImpl : public ModelObj {
     IntTuple seq_ids_tuple(seq_ids);
     IntTuple lengths_tuple(lengths.begin(), lengths.end());
     IntTuple token_tree_parent_ptr_tuple(token_tree_parent_ptr);
-    ft_.kv_cache_begin_forward_func_(kv_cache_, seq_ids_tuple, lengths_tuple,
+    ft_.kv_cache_begin_forward_func_(kv_cache_, seq_ids_tuple, lengths_tuple, seqlen_padding_factor_,
                                      token_tree_parent_ptr_tuple);
 
     ObjectRef embeddings_dref_or_nd;
@@ -564,7 +575,7 @@ class ModelImpl : public ModelObj {
     // Begin forward with the sequence ids and new lengths.
     IntTuple seq_ids_tuple(seq_ids);
     IntTuple lengths_tuple(std::vector<int64_t>(/*n=*/seq_ids.size(), /*v=*/1));
-    ft_.kv_cache_begin_forward_func_(kv_cache_, seq_ids_tuple, lengths_tuple);
+    ft_.kv_cache_begin_forward_func_(kv_cache_, seq_ids_tuple, lengths_tuple, seqlen_padding_factor_);
 
     // args: embeddings, kv_cache, params
     ObjectRef result{nullptr};
@@ -624,7 +635,7 @@ class ModelImpl : public ModelObj {
     IntTuple seq_ids_tuple(seq_ids);
     IntTuple lengths_tuple(lengths.begin(), lengths.end());
     IntTuple token_tree_parent_ptr_tuple(token_tree_parent_ptr);
-    ft_.kv_cache_begin_forward_func_(kv_cache_, seq_ids_tuple, lengths_tuple,
+    ft_.kv_cache_begin_forward_func_(kv_cache_, seq_ids_tuple, lengths_tuple, seqlen_padding_factor_,
                                      token_tree_parent_ptr_tuple);
 
     ObjectRef embeddings_dref_or_nd;
@@ -712,7 +723,7 @@ class ModelImpl : public ModelObj {
     IntTuple seq_ids_tuple(seq_ids);
     IntTuple lengths_tuple(lengths.begin(), lengths.end());
     IntTuple token_tree_parent_ptr_tuple(token_tree_parent_ptr);
-    ft_.kv_cache_begin_forward_func_(kv_cache_, seq_ids_tuple, lengths_tuple,
+    ft_.kv_cache_begin_forward_func_(kv_cache_, seq_ids_tuple, lengths_tuple, seqlen_padding_factor_,
                                      token_tree_parent_ptr_tuple);
 
     // args: embeddings, logit_pos, kv_cache, params
@@ -827,7 +838,7 @@ class ModelImpl : public ModelObj {
 
     // Run KV receive preparation.
     ObjectRef ret;
-    ret = ft_.kv_cache_disagg_prepare_recv_func_(kv_cache_, seq_id, length).cast<ObjectRef>();
+    ret = ft_.kv_cache_disagg_prepare_recv_func_(kv_cache_, seq_id, length, seqlen_padding_factor_).cast<ObjectRef>();
     IntTuple compressed_kv_append_metadata;
     if (ft_.use_disco) {
       compressed_kv_append_metadata = Downcast<DRef>(ret)->DebugGetFromRemote(0).cast<IntTuple>();