diff --git a/hpu-utils/profile_forward.py b/hpu-utils/profile_forward.py
new file mode 100644
index 0000000000000..609641e714f9e
--- /dev/null
+++ b/hpu-utils/profile_forward.py
@@ -0,0 +1,122 @@
+###############################################################################
+# Copyright (C) 2024 Habana Labs, Ltd. an Intel Company
+###############################################################################
+ 
+import argparse
+import torch
+import os
+import glob
+import shutil
+ 
+os.environ['VLLM_SKIP_WARMUP']='true'
+from vllm import LLM, SamplingParams
+from vllm.sequence import SequenceData, SequenceGroupMetadata, ExecuteModelRequest
+from multiprocessing import Process
+ 
+def setup_profiler(steps):
+    activities = [torch.profiler.ProfilerActivity.CPU]
+    activities.extend([torch.profiler.ProfilerActivity.HPU])
+    wait = 0
+    active = 1
+    warmup = steps - active
+ 
+    schedule = torch.profiler.schedule(wait=wait, warmup=warmup, active=active, repeat=1)
+    profiler = torch.profiler.profile(
+        schedule=schedule,
+        activities=activities,
+        on_trace_ready=torch.profiler.tensorboard_trace_handler('.', use_gzip=True),
+        record_shapes=False,
+        with_stack=True)
+    return profiler
+ 
+def profiler_files_organise(output_file):
+    """Changes new profiling file to specified path"""    
+    profiler_files = glob.glob('./*.json.gz')
+    latest_file = max(profiler_files, key=os.path.getctime)
+    os.makedirs(os.path.dirname(output_file), exist_ok=True)
+    shutil.move(latest_file, output_file)
+ 
+def kill_process(pid):
+    """Kills python3 main process manually"""
+    print("Killing process manually")
+    import psutil
+    for proc in psutil.process_iter():
+        if proc.pid == pid:
+            proc.kill()
+ 
+def round_up(n, k):
+    return ((n + k - 1) // k) * k
+ 
+def run_forward(llm, is_prompt, block_size, batch_size, seq_len):
+    """Single forward run"""
+    sampling_params = SamplingParams(temperature=0)
+    seqs = []
+    if is_prompt:
+        input_len = seq_len
+        output_len = 0
+    else:
+        input_len = seq_len - 1
+        output_len = 1
+ 
+    for group_id in range(batch_size):
+        prompt_token_ids = [0] * input_len
+        output_token_ids = [1] * output_len
+        block_tables = {group_id: [0] * (round_up(seq_len, block_size) // block_size)}
+        seq_data = SequenceData(prompt_token_ids)
+        seq_data.output_token_ids = output_token_ids
+        seq = SequenceGroupMetadata(
+            request_id=str(group_id),
+            is_prompt=(output_len == 0),
+            seq_data={group_id: seq_data},
+            sampling_params=sampling_params,
+            block_tables=block_tables,
+        )
+        seqs.append(seq)
+ 
+    model_request = ExecuteModelRequest(seq_group_metadata_list=seqs)
+   
+    llm.llm_engine.model_executor.execute_model(model_request)
+ 
+    print("Forward completed")
+ 
+def run_vllm(model_dtype, is_prompt, args):
+    """vLLM setup and run"""
+    llm = LLM(model=args.model_path, enforce_eager=True, dtype=model_dtype, block_size=args.block_size, tensor_parallel_size=args.num_cards)
+    profiler = setup_profiler(args.steps)
+    profiler.start()
+    print("Starting steps")
+    for _ in range(args.steps):
+        run_forward(llm, is_prompt, args.block_size, args.batch_size, args.seq_len)
+        profiler.step()
+    profiler.stop()
+    print("Finished running llm")
+ 
+parser = argparse.ArgumentParser("vLLM arguments parser", formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+ 
+parser.add_argument("--model-path", help="Path to the model that will be used", type=str, required=True)
+parser.add_argument("--num-cards", help="Number of cards that will be used by model", type=int, default=1)
+parser.add_argument("--phase", help="Phase", type=str, choices=["prompt", "decode"], default="decode")
+parser.add_argument("--data-type", help="Type of data that will be used", type=str, default="bf16", choices=["bf16"])
+parser.add_argument("--output-path", help="Path where profiler file will be stored", type=str, default="./output.json.gz")
+parser.add_argument("--block-size", help="Block size", type=int, default=128)
+parser.add_argument("--batch-size", help="Batch size", type=int, default=32)
+parser.add_argument("--seq-len", help="Sequence length", type=int, default=1024)
+parser.add_argument("--steps", help="Number of steps", type=int, default=3)
+args = parser.parse_args()
+ 
+print(args)
+ 
+if args.data_type == "bf16":
+    model_dtype = torch.bfloat16
+ 
+is_prompt = args.phase == "prompt"
+ 
+pid = os.getpid()
+ 
+run_vllm(model_dtype, is_prompt, args)
+ 
+profiler_files_organise(args.output_path)
+ 
+print("Done")
+ 
+kill_process(pid)
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