diff --git a/tests/full_tests/ci_gsm8k_tests.sh b/tests/full_tests/ci_gsm8k_tests.sh
index 8225926295..0cba7bec7d 100644
--- a/tests/full_tests/ci_gsm8k_tests.sh
+++ b/tests/full_tests/ci_gsm8k_tests.sh
@@ -99,7 +99,7 @@ run_qwen3_compressed_tensor_dynamic_scaling_test() {
 # QWEN3 FP8 + MOE compressed tensor + dynamic scaling
 run_qwen3_moe_compressed_tensor_dynamic_scaling_test() {
     echo "➡️ Testing Qwen/Qwen3-30B-A3B-Instruct-2507-FP8 + moe + compressed-tensor + dynamic scaling..."
-    HABANA_VISIBLE_DEVICES=all VLLM_CONTIGUOUS_PA=False VLLM_SKIP_WARMUP=true PT_HPU_LAZY_MODE=1 python -u "${VLLM_GAUDI_PREFIX}/tests/full_tests/generate.py" --model Qwen/Qwen3-30B-A3B-Instruct-2507-FP8 --trust-remote-code
+    HABANA_VISIBLE_DEVICES=all VLLM_CONTIGUOUS_PA=False VLLM_SKIP_WARMUP=true PT_HPU_LAZY_MODE=1 python -u "${VLLM_GAUDI_PREFIX}/tests/full_tests/generate.py" --model Qwen/Qwen3-30B-A3B-Instruct-2507-FP8 --trust-remote-code --max-model-len 131072
     echo "✅ Test with Qwen/Qwen3-30B-A3B-Instruct-2507-FP8 + moe + compressed-tensor + dynamic scaling successful."
 }
 
diff --git a/tests/full_tests/ci_perf_tests.sh b/tests/full_tests/ci_perf_tests.sh
index fb94a19565..2066572eea 100644
--- a/tests/full_tests/ci_perf_tests.sh
+++ b/tests/full_tests/ci_perf_tests.sh
@@ -37,4 +37,4 @@ vllm bench throughput \
   --dataset_path ShareGPT_V3_unfiltered_cleaned_split.json \
   --dataset_name sharegpt \
   --num-prompts 1000 \
-  --max-model-len 32768
+  --max-model-len 16384
diff --git a/vllm_gaudi/v1/worker/hpu_model_runner.py b/vllm_gaudi/v1/worker/hpu_model_runner.py
index c9488523e7..ed650597af 100644
--- a/vllm_gaudi/v1/worker/hpu_model_runner.py
+++ b/vllm_gaudi/v1/worker/hpu_model_runner.py
@@ -786,14 +786,14 @@ def __init__(
         self.use_hpu_graph = not self.model_config.enforce_eager
         self.max_batch_size = self.scheduler_config.max_num_seqs
         self.max_num_seqs = self.scheduler_config.max_num_seqs
+        self.max_cudagraph_capture_size = self.vllm_config.compilation_config.max_cudagraph_capture_size
         if prompt_profile_cfg:
             self.max_prefill_batch_size = prompt_profile_cfg[0]
         else:
             self.max_prefill_batch_size = with_default(get_config().VLLM_PROMPT_BS_BUCKET_MAX, 1)
         self.seen_configs: set = set()
-        self.max_num_batched_tokens = self.scheduler_config.max_num_batched_tokens
-        self.max_graph_capture_tokens = self.vllm_config.compilation_config.max_cudagraph_capture_size if \
-            self.vllm_config.compilation_config.max_cudagraph_capture_size is not None else self.max_num_batched_tokens
+        self.max_num_batched_tokens = \
+            self.scheduler_config.max_num_batched_tokens
         self.use_prefix_caching = (self.vllm_config.cache_config.enable_prefix_caching)
         self.bucketing_manager = HPUBucketingManager()
         max_num_prefill_seqs = self.max_num_seqs if self.use_merged_prefill \
@@ -2603,9 +2603,7 @@ def _execute_model_generic(self,
         additional_kwargs = {}
         if htorch.utils.internal.is_lazy():
             use_graphs = self._use_graphs()
-            # skip HPU graphs for long prefills
-            if seq_len > 1 and \
-                batch_size * (seq_len + num_blocks * self.block_size) > self.max_graph_capture_tokens:
+            if self.max_cudagraph_capture_size is not None and batch_size * seq_len > self.max_cudagraph_capture_size:
                 use_graphs = False
             additional_kwargs.update({"bypass_hpu_graphs": not use_graphs})
         else: