diff --git a/tests/models/multimodal/generation/test_maverick.py b/tests/models/multimodal/generation/test_maverick.py
new file mode 100644
index 000000000000..083dc66148e2
--- /dev/null
+++ b/tests/models/multimodal/generation/test_maverick.py
@@ -0,0 +1,649 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Create a reduced-layer version of the Maverick model for testing purposes.
+
+This script creates a new model with fewer layers by:
+1. Loading the original Maverick model configuration
+2. Creating a reduced configuration
+3. Generating compatible safetensors files with appropriate weights
+4. Creating the necessary index files for vLLM compatibility
+"""
+
+import json
+import shutil
+from pathlib import Path
+from typing import Any
+
+import pytest
+import torch
+from safetensors.torch import save_file
+from transformers import (AutoConfig, AutoProcessor, AutoTokenizer,
+                          GenerationConfig)
+
+from vllm import LLM, SamplingParams
+
+# Sample prompts for testing
+PROMPTS: list[str] = [
+    "Hello, my name is",
+    "The president of the United States is",
+    "The capital of France is",
+    "The future of AI is",
+]
+
+
+def run_maverick_serving(model: str):
+    """Test Llama-4-Maverick model with vLLM LLM class using CLI equivalent
+    options with reduced layers.
+    """
+
+    try:
+        sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
+
+        llm = LLM(
+            model=model,
+            max_model_len=2048,
+            enforce_eager=True,
+            tensor_parallel_size=8,
+            enable_expert_parallel=True,
+            trust_remote_code=True,
+            gpu_memory_utilization=0.4,
+            kv_cache_dtype="fp8",
+        )
+
+        outputs = llm.generate(PROMPTS, sampling_params)
+
+        # Print the outputs
+        print("\nGenerated Outputs:\n" + "-" * 60)
+        for output in outputs:
+            prompt = output.prompt
+            generated_text = output.outputs[0].text
+            print(f"Prompt:    {prompt!r}")
+            print(f"Output:    {generated_text!r}")
+            print("-" * 60)
+
+    except Exception as e:
+        print(f"Error initializing or running model: {e}")
+        raise
+
+
+def create_reduced_maverick_model(
+    original_model_name:
+    str = "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
+    output_dir: str = "/tmp/reduced_maverick",
+    text_layers: int = 4,
+    num_experts: int = 4,
+    vision_layers: int = 2,
+    force_recreate: bool = False,
+) -> str:
+    """
+    Create a reduced-layer version of the Maverick model.
+
+    Args:
+        original_model_name: Name of the original Maverick model
+        output_dir: Directory to save the reduced model
+        text_layers: Number of text transformer layers
+        num_experts: Number of experts per layer
+        vision_layers: Number of vision transformer layers
+        force_recreate: Whether to recreate if output_dir already exists
+
+    Returns:
+        Path to the created reduced model directory
+    """
+
+    print(
+        f"Creating reduced Maverick model with {text_layers} text layers and "
+        f"{vision_layers} vision layers...")
+
+    # Create output directory
+    output_path = Path(output_dir)
+    if output_path.exists():
+        if force_recreate:
+            shutil.rmtree(output_path)
+        else:
+            print(f"Output directory {output_dir} already exists. "
+                  "Use --force-recreate to overwrite.")
+            return str(output_path)
+
+    output_path.mkdir(parents=True, exist_ok=True)
+
+    try:
+        print("Loading original model configuration...")
+        original_config = AutoConfig.from_pretrained(original_model_name,
+                                                     trust_remote_code=True)
+
+        print("Creating reduced configuration...")
+        reduced_config = create_reduced_config(original_config, text_layers,
+                                               num_experts, vision_layers)
+
+        config_path = output_path / "config.json"
+        with open(config_path, "w") as f:
+            json.dump(reduced_config, f, indent=2)
+        print(f"Saved reduced config to {config_path}")
+
+        print("Copying tokenizer files...")
+        copy_tokenizer_files(original_model_name, output_path)
+
+        print("Creating reduced safetensors files...")
+        create_reduced_safetensors(original_config, reduced_config,
+                                   output_path)
+
+        print("Creating preprocessor config...")
+        create_preprocessor_config(original_config, output_path)
+
+        try:
+            gen_config = GenerationConfig.from_pretrained(original_model_name)
+            gen_config.save_pretrained(output_path)
+            print("Copied generation config")
+        except Exception as e:
+            print(f"Could not copy generation config: {e}")
+
+        print(f"Successfully created reduced Maverick model at {output_path}")
+        return str(output_path)
+
+    except Exception as e:
+        print(f"Error creating reduced model: {e}")
+        # Clean up on failure
+        if output_path.exists():
+            shutil.rmtree(output_path)
+        raise
+
+
+def create_reduced_config(original_config: Any, text_layers: int,
+                          num_experts: int,
+                          vision_layers: int) -> dict[str, Any]:
+    """Create a reduced configuration based on the original."""
+
+    # Convert config to dictionary
+    config_dict = original_config.to_dict()
+
+    # Reduce text layers
+    if "text_config" in config_dict:
+        original_text_layers = config_dict["text_config"]["num_hidden_layers"]
+        config_dict["text_config"]["num_hidden_layers"] = text_layers
+        print(
+            f"Reduced text layers from {original_text_layers} to {text_layers}"
+        )
+
+        original_num_experts = config_dict["text_config"]["num_local_experts"]
+        config_dict["text_config"]["num_local_experts"] = num_experts
+        print(
+            f"Reduced num experts from {original_num_experts} to {num_experts}"
+        )
+
+        hidden_dim_divisor = 4
+
+        original_hidden_size = config_dict["text_config"]["hidden_size"]
+        new_hidden_size = original_hidden_size // hidden_dim_divisor
+        config_dict["text_config"]["hidden_size"] = new_hidden_size
+        print(f"Reduced hidden size from {original_hidden_size} to "
+              f"{new_hidden_size}")
+
+        original_head_dim = config_dict["text_config"]["head_dim"]
+        new_head_dim = original_head_dim // hidden_dim_divisor
+        config_dict["text_config"]["head_dim"] = new_head_dim
+        print(f"Reduced head dim from {original_head_dim} to {new_head_dim}")
+
+    # Reduce vision layers
+    if "vision_config" in config_dict:
+        original_vision_layers = config_dict["vision_config"][
+            "num_hidden_layers"]
+        config_dict["vision_config"]["num_hidden_layers"] = vision_layers
+        print(f"Reduced vision layers from {original_vision_layers} "
+              f"to {vision_layers}")
+
+    # Update model name to indicate it's a reduced version
+    config_dict["_name_or_path"] = (
+        f"reduced_maverick_{text_layers}t_{vision_layers}v")
+
+    return config_dict
+
+
+def copy_tokenizer_files(original_model_name: str, output_path: Path) -> None:
+    """Copy tokenizer files from the original model."""
+
+    try:
+        tokenizer = AutoTokenizer.from_pretrained(original_model_name,
+                                                  trust_remote_code=True)
+        tokenizer.save_pretrained(output_path)
+        print("Tokenizer files copied successfully")
+    except Exception as e:
+        print(f"Warning: Could not copy tokenizer files: {e}")
+
+
+def create_preprocessor_config(original_config: Any,
+                               output_path: Path) -> None:
+    """Create preprocessor_config.json for multimodal model."""
+
+    # Try to load the original preprocessor config
+    try:
+        processor = AutoProcessor.from_pretrained(
+            original_config._name_or_path
+            or "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
+            trust_remote_code=True,
+        )
+        processor.save_pretrained(output_path)
+        print("Copied original preprocessor config")
+        return
+    except Exception as e:
+        print(f"Could not copy original preprocessor config: {e}")
+        raise
+
+
+def create_reduced_safetensors(original_config: Any, reduced_config: dict[str,
+                                                                          Any],
+                               output_path: Path) -> None:
+    """Create safetensors files with weights for the reduced model."""
+
+    print("Generating synthetic weights for reduced model...")
+
+    text_config = reduced_config["text_config"]
+    vision_config = reduced_config["vision_config"]
+
+    weights = {}
+
+    print("Creating text model weights...")
+    weights.update(create_text_model_weights(text_config))
+
+    print("Creating vision model weights...")
+    weights.update(create_vision_model_weights(vision_config))
+
+    print("Creating shared model weights...")
+    weights.update(create_shared_weights(text_config, vision_config))
+
+    print("Saving weights to safetensors files...")
+    save_weights_to_safetensors(weights, output_path)
+
+
+def create_text_model_weights(
+        text_config: dict[str, Any]) -> dict[str, torch.Tensor]:
+    """Create synthetic weights for the text model with MoE structure."""
+
+    weights = {}
+
+    vocab_size = text_config["vocab_size"]
+    hidden_size = text_config["hidden_size"]
+    intermediate_size = text_config["intermediate_size"]
+    intermediate_size_mlp = text_config["intermediate_size_mlp"]
+    num_layers = text_config["num_hidden_layers"]
+    num_attention_heads = text_config["num_attention_heads"]
+    num_key_value_heads = text_config.get("num_key_value_heads",
+                                          num_attention_heads)
+
+    # MoE specific parameters
+    num_experts = text_config.get("num_local_experts")
+    assert (num_experts
+            is not None), "num_local_experts must be specified for MoE"
+
+    head_dim = hidden_size // num_attention_heads
+
+    # Embedding layers
+    weights["language_model.model.embed_tokens.weight"] = torch.randn(
+        vocab_size, hidden_size, dtype=torch.float16)
+
+    # Transformer layers
+    for layer_idx in range(num_layers):
+        layer_prefix = f"language_model.model.layers.{layer_idx}"
+        print(f"Creating weights for layer {layer_prefix}...")
+
+        # Self-attention weights (separate q, k, v projections)
+        weights[f"{layer_prefix}.self_attn.q_proj.weight"] = torch.randn(
+            hidden_size, num_attention_heads * head_dim, dtype=torch.bfloat16)
+        weights[f"{layer_prefix}.self_attn.k_proj.weight"] = torch.randn(
+            hidden_size, num_key_value_heads * head_dim, dtype=torch.bfloat16)
+        weights[f"{layer_prefix}.self_attn.v_proj.weight"] = torch.randn(
+            num_key_value_heads * head_dim, hidden_size, dtype=torch.bfloat16)
+        weights[f"{layer_prefix}.self_attn.o_proj.weight"] = torch.randn(
+            hidden_size, num_attention_heads * head_dim, dtype=torch.bfloat16)
+        print("Self-attention weights created.")
+
+        # Feed-forward weights - MoE pattern based on interleave_moe_layer_step
+        # For interleave_moe_layer_step=2: layers 1,3,5,... are MoE, layers
+        # 0,2,4,... are dense
+        interleave_step = text_config.get("interleave_moe_layer_step", 1)
+        is_moe_layer = (interleave_step > 0
+                        and (layer_idx + 1) % interleave_step == 0)
+
+        if is_moe_layer:
+            # MoE layer structure
+            # 1. Router weights
+            weights[
+                f"{layer_prefix}.feed_forward.router.weight"] = torch.randn(
+                    num_experts, hidden_size, dtype=torch.float16)
+
+            # 2. Individual expert weights (not fused)
+            for expert_idx in range(num_experts):
+                expert_prefix = (
+                    f"{layer_prefix}.feed_forward.experts.{expert_idx}")
+
+                weights[f"{expert_prefix}.gate_proj.weight"] = torch.randn(
+                    intermediate_size, hidden_size, dtype=torch.bfloat16)
+                weights[f"{expert_prefix}.up_proj.weight"] = torch.randn(
+                    intermediate_size, hidden_size, dtype=torch.bfloat16)
+                weights[f"{expert_prefix}.down_proj.weight"] = torch.randn(
+                    hidden_size, intermediate_size, dtype=torch.bfloat16)
+
+                # Expert weight scales (FP8 quantization)
+                weights[
+                    f"{expert_prefix}.gate_proj.weight_scale"] = torch.ones(
+                        intermediate_size, 1, dtype=torch.bfloat16)
+                weights[f"{expert_prefix}.up_proj.weight_scale"] = torch.ones(
+                    intermediate_size, 1, dtype=torch.bfloat16)
+                weights[
+                    f"{expert_prefix}.down_proj.weight_scale"] = torch.ones(
+                        hidden_size, 1, dtype=torch.bfloat16)
+
+            # 3. Shared expert weights
+            shared_expert_prefix = f"{layer_prefix}.feed_forward.shared_expert"
+            weights[f"{shared_expert_prefix}.gate_proj.weight"] = torch.randn(
+                intermediate_size, hidden_size, dtype=torch.bfloat16)
+            weights[f"{shared_expert_prefix}.up_proj.weight"] = torch.randn(
+                intermediate_size, hidden_size, dtype=torch.bfloat16)
+            weights[f"{shared_expert_prefix}.down_proj.weight"] = torch.randn(
+                hidden_size, intermediate_size, dtype=torch.bfloat16)
+            print(f"MoE feed-forward weights created for layer {layer_idx}.")
+        else:
+            # Dense layer structure
+            weights[f"{layer_prefix}.feed_forward.gate_proj.weight"] = (
+                torch.randn(intermediate_size_mlp,
+                            hidden_size,
+                            dtype=torch.bfloat16))
+            weights[f"{layer_prefix}.feed_forward.up_proj.weight"] = (
+                torch.randn(intermediate_size_mlp,
+                            hidden_size,
+                            dtype=torch.bfloat16))
+            weights[f"{layer_prefix}.feed_forward.down_proj.weight"] = (
+                torch.randn(hidden_size,
+                            intermediate_size_mlp,
+                            dtype=torch.bfloat16))
+            print(f"Dense feed-forward weights created for layer {layer_idx}.")
+
+        # Layer norms
+        weights[f"{layer_prefix}.input_layernorm.weight"] = torch.ones(
+            hidden_size, dtype=torch.bfloat16)
+        weights[
+            f"{layer_prefix}.post_attention_layernorm.weight"] = torch.ones(
+                hidden_size, dtype=torch.bfloat16)
+        print("Layer norms created.")
+
+    # Final layer norm and output projection
+    weights["language_model.model.norm.weight"] = torch.ones(
+        hidden_size, dtype=torch.bfloat16)
+    weights["language_model.lm_head.weight"] = torch.randn(
+        vocab_size, hidden_size, dtype=torch.bfloat16)
+
+    return weights
+
+
+def create_vision_model_weights(
+        vision_config: dict[str, Any]) -> dict[str, torch.Tensor]:
+    """Create synthetic weights for the vision model."""
+
+    weights = {}
+
+    hidden_size = vision_config["hidden_size"]
+    intermediate_size = vision_config["intermediate_size"]
+    num_layers = vision_config["num_hidden_layers"]
+
+    # Vision transformer layers
+    for layer_idx in range(num_layers):
+        layer_prefix = f"vision_model.model.layers.{layer_idx}"
+
+        weights[f"{layer_prefix}.self_attn.q_proj.weight"] = torch.randn(
+            hidden_size, hidden_size, dtype=torch.bfloat16)
+        weights[f"{layer_prefix}.self_attn.q_proj.bias"] = torch.zeros(
+            hidden_size, dtype=torch.bfloat16)
+        weights[f"{layer_prefix}.self_attn.k_proj.weight"] = torch.randn(
+            hidden_size, hidden_size, dtype=torch.bfloat16)
+        weights[f"{layer_prefix}.self_attn.k_proj.bias"] = torch.zeros(
+            hidden_size, dtype=torch.bfloat16)
+        weights[f"{layer_prefix}.self_attn.v_proj.weight"] = torch.randn(
+            hidden_size, hidden_size, dtype=torch.bfloat16)
+        weights[f"{layer_prefix}.self_attn.v_proj.bias"] = torch.zeros(
+            hidden_size, dtype=torch.bfloat16)
+        weights[f"{layer_prefix}.self_attn.o_proj.weight"] = torch.randn(
+            hidden_size, hidden_size, dtype=torch.bfloat16)
+        weights[f"{layer_prefix}.self_attn.o_proj.bias"] = torch.zeros(
+            hidden_size, dtype=torch.bfloat16)
+
+        weights[f"{layer_prefix}.mlp.fc1.weight"] = torch.randn(
+            intermediate_size, hidden_size, dtype=torch.bfloat16)
+        weights[f"{layer_prefix}.mlp.fc1.bias"] = torch.zeros(
+            intermediate_size, dtype=torch.bfloat16)
+        weights[f"{layer_prefix}.mlp.fc2.weight"] = torch.randn(
+            hidden_size, intermediate_size, dtype=torch.bfloat16)
+        weights[f"{layer_prefix}.mlp.fc2.bias"] = torch.zeros(
+            hidden_size, dtype=torch.bfloat16)
+
+        weights[f"{layer_prefix}.input_layernorm.weight"] = torch.ones(
+            hidden_size, dtype=torch.bfloat16)
+        weights[f"{layer_prefix}.input_layernorm.bias"] = torch.zeros(
+            hidden_size, dtype=torch.bfloat16)
+        weights[
+            f"{layer_prefix}.post_attention_layernorm.weight"] = torch.ones(
+                hidden_size, dtype=torch.bfloat16)
+        weights[f"{layer_prefix}.post_attention_layernorm.bias"] = torch.zeros(
+            hidden_size, dtype=torch.bfloat16)
+
+    return weights
+
+
+def create_shared_weights(
+        text_config: dict[str, Any],
+        vision_config: dict[str, Any]) -> dict[str, torch.Tensor]:
+    """Create weights for shared components (vision-language connector)"""
+
+    weights = {}
+
+    text_hidden_size = text_config["hidden_size"]
+    projector_input_dim = vision_config["projector_input_dim"]
+
+    # Vision-language connector (projects vision features to text space)
+    weights["multi_modal_projector.linear_1.weight"] = torch.randn(
+        text_hidden_size, projector_input_dim, dtype=torch.bfloat16)
+
+    return weights
+
+
+def save_weights_to_safetensors(weights: dict[str, torch.Tensor],
+                                output_path: Path) -> None:
+    """Save weights to safetensors files and create index."""
+
+    # Determine how to shard the weights
+    max_shard_size = 5 * 1024 * 1024 * 1024  # 5GB per shard
+
+    # Calculate sizes and create shards
+    shards = []
+    current_shard: dict[str, torch.Tensor] = {}
+    current_size = 0
+
+    for name, tensor in weights.items():
+        tensor_size = tensor.numel() * tensor.element_size()
+
+        if current_size + tensor_size > max_shard_size and current_shard:
+            shards.append(current_shard)
+            current_shard = {}
+            current_size = 0
+
+        current_shard[name] = tensor
+        current_size += tensor_size
+
+    if current_shard:
+        shards.append(current_shard)
+
+    # Save shards and create index
+    weight_map = {}
+
+    if len(shards) == 1:
+        # Single file
+        filename = "model.safetensors"
+        save_file(shards[0], output_path / filename)
+        weight_map = {name: filename for name in shards[0]}
+        print(f"Saved weights to single file: {filename}")
+    else:
+        # Multiple shards
+        for i, shard in enumerate(shards):
+            filename = f"model-{i+1:05d}-of-{len(shards):05d}.safetensors"
+            save_file(shard, output_path / filename)
+            for name in shard:
+                weight_map[name] = filename
+            print(f"Saved shard {i+1}/{len(shards)}: {filename}")
+
+    # Create index file
+    index_data = {
+        "metadata": {
+            "total_size":
+            sum(tensor.numel() * tensor.element_size()
+                for tensor in weights.values())
+        },
+        "weight_map": weight_map,
+    }
+
+    index_path = output_path / "model.safetensors.index.json"
+    with open(index_path, "w") as f:
+        json.dump(index_data, f, indent=2)
+
+    print(f"Created index file: {index_path}")
+    print(f"Total model size: "
+          f"{index_data['metadata']['total_size'] / (1024**3):.2f} GB")
+
+
+def run_reduced_model(model_path: str,
+                      should_profile: bool = False,
+                      **kwargs) -> None:
+    """Test the created reduced model with vLLM."""
+
+    print(f"\nTesting reduced model at {model_path}...")
+
+    llm = LLM(
+        model=model_path,
+        trust_remote_code=True,
+        max_model_len=512,  # Small context for testing
+        gpu_memory_utilization=0.3,  # Conservative memory usage
+        **kwargs,
+    )
+
+    sampling_params = SamplingParams(temperature=0.8,
+                                     top_p=0.95,
+                                     max_tokens=50)
+
+    if should_profile:
+        llm.start_profile()
+    outputs = llm.generate(PROMPTS, sampling_params)
+    if should_profile:
+        llm.stop_profile()
+
+    print("Test generation successful!")
+    for output in outputs:
+        print(f"Prompt: {output.prompt}")
+        print(f"Output: "
+              f"{output.outputs[0].text}")
+        print("-" * 40)
+
+
+@pytest.mark.parametrize(
+    "original_model_name,text_layers,num_experts,vision_layers,",
+    [("meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8", 4, 4, 2)])
+@pytest.mark.parametrize("enforce_eager", [True, False])
+@pytest.mark.parametrize("tp,ep", [(2, True)])
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
+def test_dummy_maverick(
+    original_model_name: str,
+    text_layers: int,
+    num_experts: int,
+    vision_layers: int,
+    enforce_eager: bool,
+    tp: int,
+    ep: bool,
+    output_dir: str = "/tmp/reduced_maverick",
+    force_recreate: bool = True,
+    profile: bool = False,
+) -> None:
+    model_path = create_reduced_maverick_model(
+        original_model_name=original_model_name,
+        output_dir=output_dir,
+        text_layers=text_layers,
+        num_experts=num_experts,
+        vision_layers=vision_layers,
+        force_recreate=force_recreate,
+    )
+
+    print(f"\nReduced model created successfully at: {model_path}")
+
+    run_reduced_model(model_path=model_path,
+                      should_profile=profile,
+                      enforce_eager=enforce_eager,
+                      tensor_parallel_size=tp,
+                      enable_expert_parallel=ep)
+
+
+def main():
+    """Main function to create and test the reduced model."""
+
+    import argparse
+
+    parser = argparse.ArgumentParser(
+        description="Create a reduced-layer Maverick model")
+    parser.add_argument(
+        "--output-dir",
+        default="/tmp/reduced_maverick",
+        help="Output directory for the reduced model",
+    )
+    parser.add_argument(
+        "--text-layers",
+        type=int,
+        default=4,
+        help="Number of text transformer layers",
+    )
+    parser.add_argument("--num-experts",
+                        type=int,
+                        default=4,
+                        help="Number of experts")
+    parser.add_argument(
+        "--vision-layers",
+        type=int,
+        default=2,
+        help="Number of vision transformer layers",
+    )
+    parser.add_argument(
+        "--force-recreate",
+        action="store_true",
+        help="Force recreation if output directory exists",
+    )
+    parser.add_argument("--test",
+                        action="store_true",
+                        help="Test the created model with vLLM")
+    parser.add_argument("--profile",
+                        action="store_true",
+                        help="Profile the created model with vLLM")
+    parser.add_argument(
+        "--test-original",
+        action="store_true",
+        help="Test the original model with vLLM",
+    )
+    parser.add_argument(
+        "--original-model",
+        default="meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
+        help="Original model name to base the reduction on",
+    )
+
+    args = parser.parse_args()
+
+    if args.test:
+        test_dummy_maverick(original_model_name=args.original_model,
+                            output_dir=args.output_dir,
+                            text_layers=args.text_layers,
+                            num_experts=args.num_experts,
+                            vision_layers=args.vision_layers,
+                            force_recreate=args.force_recreate,
+                            tp=2,
+                            ep=True,
+                            enforce_eager=True,
+                            profile=args.profile)
+
+    if args.test_original:
+        run_maverick_serving(args.original_model)
+
+
+if __name__ == "__main__":
+    exit(main())