diff --git a/measure_vram.py b/measure_vram.py
new file mode 100644
index 0000000..26e99c9
--- /dev/null
+++ b/measure_vram.py
@@ -0,0 +1,177 @@
+import logging
+import wandb
+from functools import partial
+
+import torch
+from datasets import load_dataset
+from torch.utils.data import DataLoader
+from transformers import AutoProcessor, Gemma3ForConditionalGeneration, BitsAndBytesConfig
+
+from config import Configuration
+from utils import train_collate_function
+from peft import prepare_model_for_kbit_training, LoraConfig, get_peft_model, PeftType
+
+
+import albumentations as A
+
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+)
+logger = logging.getLogger(__name__)
+
+cfg = Configuration()
+
+augmentations = A.Compose([
+    A.Resize(height=896, width=896),
+    A.HorizontalFlip(p=0.5),
+    A.ColorJitter(p=0.2),
+], bbox_params=A.BboxParams(format='coco', label_fields=['category_ids'], filter_invalid_bboxes=True))
+
+
+def initialize_model():
+    cfg = Configuration()
+
+    logger.info("Getting model & turning only attention parameters to trainable")
+
+    bnb_config = BitsAndBytesConfig(
+        load_in_4bit=True,
+        bnb_4bit_use_double_quant=True,
+        bnb_4bit_quant_type="nf4",
+        bnb_4bit_compute_dtype=torch.bfloat16
+    )
+
+    model = Gemma3ForConditionalGeneration.from_pretrained(
+        cfg.model_id,
+        torch_dtype=cfg.dtype,
+        device_map="cpu",
+        attn_implementation="eager",
+        quantization_config=bnb_config
+    )
+
+    model = prepare_model_for_kbit_training(model)
+
+    lora_config = LoraConfig(
+        inference_mode=False,
+        r=8,
+        lora_alpha=32,
+        lora_dropout=0.1,
+        target_modules=["q_proj", "v_proj", "k_proj", "o_proj"],
+        peft_type=PeftType.LORA,
+    )
+
+    qlora_model = get_peft_model(model, lora_config)
+    qlora_model.print_trainable_parameters()
+
+    qlora_model.to(cfg.device)
+
+    return qlora_model
+
+
+def main():
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    if not torch.cuda.is_available():
+        print("CUDA not available. VRAM measurement requires a CUDA-enabled GPU.")
+        return
+
+    qlora_model = initialize_model()
+
+    # Measure VRAM after model is loaded to device
+    torch.cuda.synchronize() # Ensure all operations are complete
+    initial_vram_allocated_bytes = torch.cuda.memory_allocated(device)
+    initial_vram_allocated_mb = initial_vram_allocated_bytes / (1024 ** 2)
+    print(f"VRAM allocated after loading model to device: {initial_vram_allocated_mb:.2f} MB")
+
+    # --- Dataset Preparation ---
+    processor = AutoProcessor.from_pretrained(cfg.model_id)
+    train_dataset = load_dataset(cfg.dataset_id, split="train")
+    train_collate_fn = partial(
+        train_collate_function, processor=processor, dtype=cfg.dtype, transform=augmentations
+    )
+   
+
+    batch_sizes_to_test = [int(bs) for bs in cfg.batch_sizes]
+    if not batch_sizes_to_test:
+        print("Error: No batch sizes provided or parsed correctly.")
+        return
+    
+    num_iterations_for_vram = cfg.num_iterations
+
+
+    print("\n--- VRAM Measurement ---")
+    results = {}
+
+    for bs in batch_sizes_to_test:
+        print(f"\nTesting Batch Size: {bs}")
+
+        if len(train_dataset) < bs:
+            print(f"Base processed dataset has {len(train_dataset)} samples, "
+                  f"not enough for batch size {bs}. Skipping.")
+            results[bs] = "Not enough data"
+            continue
+
+        train_dataloader = DataLoader(
+            train_dataset,
+            batch_size=bs,
+            collate_fn=train_collate_fn,
+            shuffle=True,
+        )
+
+        if len(train_dataloader) < num_iterations_for_vram:
+            print(f"Dataloader for batch size {bs} yields {len(train_dataloader)} batches, "
+                   f"less than requested {num_iterations_for_vram} iterations. Will run available batches.")
+            if len(train_dataloader) == 0:
+                print(f"Dataloader for batch size {bs} is empty. Skipping.")
+                results[bs] = "Dataloader empty"
+                continue
+
+        # Reset CUDA memory stats for each batch size test
+        torch.cuda.reset_peak_memory_stats(device)        
+    
+        # Credits to Sayak Paul for this beautiful expression
+        qlora_model.train()
+        params_to_train = list(filter(lambda x: x.requires_grad, qlora_model.parameters()))
+        optimizer = torch.optim.AdamW(params_to_train, lr=cfg.learning_rate)
+
+
+        try:
+            for i, batch in enumerate(train_dataloader):
+                if i >= num_iterations_for_vram:
+                    break
+                
+                batch = {k: v.to(device) for k, v in batch.items()}
+
+                outputs = qlora_model(**batch)
+                loss = outputs.loss
+                loss.backward()
+                optimizer.step()
+                optimizer.zero_grad()
+            
+            peak_vram_allocated_bytes = torch.cuda.max_memory_allocated(device)
+            peak_vram_allocated_mb = peak_vram_allocated_bytes / (1024 ** 2)
+            print(f"Peak VRAM allocated for batch size {bs}: {peak_vram_allocated_mb:.2f} MB")
+            results[bs] = f"{peak_vram_allocated_mb:.2f} MB"
+        
+        except RuntimeError as e:
+            if "out of memory" in str(e).lower():
+                peak_vram_allocated_bytes = torch.cuda.max_memory_allocated(device) # Get max allocated before OOM
+                peak_vram_allocated_mb = peak_vram_allocated_bytes / (1024 ** 2)
+                print(f"CUDA out of memory for batch size {bs}. ")
+                print(f"Peak VRAM allocated before OOM: {peak_vram_allocated_mb:.2f} MB (may be approximate)")
+                results[bs] = f"OOM (Peak before OOM: {peak_vram_allocated_mb:.2f} MB)"
+            else:
+                print(f"An unexpected runtime error occurred for batch size {bs}: {e}")
+                results[bs] = f"Error: {e}"
+                # raise e # Optionally re-raise for debugging
+        
+        finally:
+            del current_loader, optimizer
+            torch.cuda.empty_cache()
+        
+    print("\n--- Summary of VRAM Usage ---")
+    for bs, vram_usage in results.items():
+        print(f"Batch Size {bs}: {vram_usage}")
+
+
+if __name__ == "__main__":
+    main()