diff --git a/vllm/model_executor/models/gemma4.py b/vllm/model_executor/models/gemma4.py
index d1498f1..745d1d3 100644
--- a/vllm/model_executor/models/gemma4.py
+++ b/vllm/model_executor/models/gemma4.py
@@ -26,7 +26,7 @@ import torch
 from torch import nn
 
 from vllm.compilation.decorators import support_torch_compile
-from vllm.config import CacheConfig, VllmConfig
+from vllm.config import CacheConfig, VllmConfig, get_current_vllm_config
 from vllm.config.utils import getattr_iter
 from vllm.distributed import (
     get_pp_group,
@@ -491,9 +491,12 @@ class Gemma4Attention(nn.Module):
                     f"{kv_shared_layer_index}.self_attn.attn"
                 )
 
+        # Gemma 4's rope types (default, proportional) do not depend on max_position, so
+        # size the cos/sin caches for max_model_len: 262144 positions take ~0.4 GiB.
+        max_model_len = get_current_vllm_config().model_config.max_model_len
         self.rotary_emb = get_rope(
             self.head_dim,
-            max_position=max_position_embeddings,
+            max_position=min(max_position_embeddings, max_model_len),
             rope_parameters=rope_parameters,
             is_neox_style=True,
         )
@@ -1444,14 +1447,17 @@ class Gemma4ForCausalLM(
             prefix=maybe_prefix(prefix, "model"),
         )
 
-        self.lm_head = ParallelLMHead(
-            config.vocab_size,
-            config.hidden_size,
-            quant_config=quant_config,
-            prefix=maybe_prefix(prefix, "lm_head"),
-        )
         if config.tie_word_embeddings:
-            self.lm_head = self.lm_head.tie_weights(self.model.embed_tokens)
+            # Share embed_tokens: a ParallelLMHead tied after construction would hold the
+            # vocab matrix (1.4 GiB) twice while loading, which overflows 8 GB GPUs.
+            self.lm_head = self.model.embed_tokens
+        else:
+            self.lm_head = ParallelLMHead(
+                config.vocab_size,
+                config.hidden_size,
+                quant_config=quant_config,
+                prefix=maybe_prefix(prefix, "lm_head"),
+            )
 
         self.logits_processor = LogitsProcessor(
             config.vocab_size,