diff --git a/python/sglang/srt/multimodal/processors/qwen_vl.py b/python/sglang/srt/multimodal/processors/qwen_vl.py
index 1bf12708a..1e50a4ca4 100644
--- a/python/sglang/srt/multimodal/processors/qwen_vl.py
+++ b/python/sglang/srt/multimodal/processors/qwen_vl.py
@@ -2,8 +2,10 @@ import asyncio
 import math
 import os
 import re
+import time
 from typing import List, Union
 
+import numpy as np
 import torch
 import torchvision
 from PIL import Image
@@ -175,12 +177,15 @@ async def preprocess_video(
     image_factor: int = IMAGE_FACTOR,
     # vr: VideoReader, image_factor: int = IMAGE_FACTOR
 ) -> torch.Tensor:
+    entry_time = time.perf_counter()
     ele = {}
     total_frames, video_fps = len(vr), vr.get_avg_fps()
     nframes = smart_nframes({}, total_frames=total_frames, video_fps=video_fps)
-    idx = torch.linspace(0, total_frames - 1, nframes).round().long().tolist()
-    video = vr.get_batch(idx).asnumpy()
-    video = torch.tensor(video).permute(0, 3, 1, 2)  # Convert to TCHW format
+    idx = np.linspace(0, total_frames - 1, num=nframes, dtype=np.int64)
+    idx = np.unique(idx)
+    video_np = vr.get_batch(idx).asnumpy()
+    video = torch.from_numpy(video_np).pin_memory()
+    video = video.permute(0, 3, 1, 2)  # Convert to TCHW format
     nframes, _, height, width = video.shape
     min_pixels = ele.get("min_pixels", VIDEO_MIN_PIXELS)
     total_pixels = ele.get("total_pixels", VIDEO_TOTAL_PIXELS)
@@ -188,6 +193,9 @@ async def preprocess_video(
         min(VIDEO_MAX_PIXELS, total_pixels / nframes * FRAME_FACTOR),
         int(min_pixels * 1.05),
     )
+
+    get_batch_time = time.perf_counter()
+
     max_pixels_supposed = ele.get("max_pixels", max_pixels)
     if max_pixels_supposed > max_pixels:
         logger.warning(
@@ -208,12 +216,13 @@ async def preprocess_video(
             min_pixels=min_pixels,
             max_pixels=max_pixels,
         )
+    smart_resize_time = time.perf_counter()
     video = torchvision.transforms.functional.resize(
         video,
         [resized_height, resized_width],
-        interpolation=InterpolationMode.BICUBIC,
-        antialias=True,
-    ).float()
+        interpolation=InterpolationMode.BILINEAR,
+    )
+    video = video.pin_memory()
     video_metadata = {
         "fps": video_fps,
         "duration": total_frames / video_fps,
@@ -221,6 +230,14 @@ async def preprocess_video(
         "frames_indices": idx,
         "video_backend": "torchvision",
     }
+    torchvision_resize_time = time.perf_counter()
+    logger.debug(
+        f"[preprocess_video Perf], "
+        f"get_batch_time: {(get_batch_time - entry_time) * 1000:.2f} ms, "
+        f"smart_resize_time: {(smart_resize_time - get_batch_time) * 1000:.2f} ms, "
+        f"torchvision_resize_time: {(torchvision_resize_time - smart_resize_time) * 1000:.2f} ms, "
+        f"total_time: {(torchvision_resize_time - entry_time) * 1000:.2f} ms"
+    )
     return video, video_metadata
 
 
@@ -273,6 +290,7 @@ class QwenVLImageProcessor(SGLangBaseProcessor):
         *args,
         **kwargs,
     ):
+        entry_time = time.perf_counter()
         base_output = self.load_mm_data(
             prompt=input_text,
             image_data=image_data,
@@ -280,6 +298,8 @@ class QwenVLImageProcessor(SGLangBaseProcessor):
             audio_data=request_obj.audio_data,
             multimodal_tokens=self.mm_tokens,
         )
+        load_time = time.perf_counter()
+        rid = getattr(request_obj, "rid", "anonymous_rid")
 
         # Qwen-specific: resize images if they are raw Image objects
         if base_output.images and isinstance(base_output.images[0], Image.Image):
@@ -288,10 +308,12 @@ class QwenVLImageProcessor(SGLangBaseProcessor):
 
         video_metadata = None
         if base_output.videos:
-            video_results = await asyncio.gather(
-                *[preprocess_video(video) for video in base_output.videos]
-            )
-            base_output.videos, video_metadata = map(list, zip(*video_results))
+            videos_processed = [
+                await preprocess_video(video) for video in base_output.videos
+            ]
+            base_output.videos, video_metadata = map(list, zip(*videos_processed))
+
+        preprocess_time = time.perf_counter()
 
         # NOTE: for qwen3-vl, video_meta need to be passed in, since do_sample_frames is already done in preprocess_video
         if self.hf_config.model_type in ("qwen3_vl", "qwen3_vl_moe"):
@@ -319,6 +341,8 @@ class QwenVLImageProcessor(SGLangBaseProcessor):
             ret, "video_second_per_grid", None
         )
 
+        process_time = time.perf_counter()
+
         input_ids = input_ids.flatten()
 
         mrope_positions, mrope_position_delta = MRotaryEmbedding.get_rope_index(
@@ -343,6 +367,15 @@ class QwenVLImageProcessor(SGLangBaseProcessor):
             ),
         )
         mrope_positions = mrope_positions.squeeze(1)
+        get_rope_index_time = time.perf_counter()
+        logger.debug(
+            f"[QwenVLProcessor Perf] {rid=}, "
+            f"load_time: {(load_time - entry_time) * 1000:.2f} ms, "
+            f"preprocess_time: {(preprocess_time - load_time) * 1000:.2f} ms, "
+            f"process_time: {(process_time - preprocess_time) * 1000:.2f} ms, "
+            f"get_rope_index_time: {(get_rope_index_time - process_time) * 1000:.2f} ms, "
+            f"total_time: {(get_rope_index_time - entry_time) * 1000:.2f} ms"
+        )
 
         return {
             "input_ids": input_ids.tolist(),