diff --git a/06_gpu_and_ml/llm-serving/sgl_vlm.py b/06_gpu_and_ml/llm-serving/sgl_vlm.py
index 0ca5432d1..10cb3c4b1 100644
--- a/06_gpu_and_ml/llm-serving/sgl_vlm.py
+++ b/06_gpu_and_ml/llm-serving/sgl_vlm.py
@@ -1,4 +1,4 @@
-# # Run LLaVA-Next on SGLang for Visual QA
+# # Run Qwen2-VL on SGLang for Visual QA
 
 # Vision-Language Models (VLMs) are like LLMs with eyes:
 # they can generate text based not just on other text,
@@ -7,7 +7,7 @@
 # This example shows how to run a VLM on Modal using the
 # [SGLang](https://github.com/sgl-project/sglang) library.
 
-# Here's a sample inference, with the image rendered directly in the terminal:
+# Here's a sample inference, with the image rendered directly (and at low resolution) in the terminal:
 
 # ![Sample output answering a question about a photo of the Statue of Liberty](https://modal-public-assets.s3.amazonaws.com/sgl_vlm_qa_sol.png)
 
@@ -32,7 +32,7 @@
 # If you want to see the model really rip, try an `"a100-80gb"` or an `"h100"`
 # on a large batch.
 
-GPU_TYPE = os.environ.get("GPU_TYPE", "a10g")
+GPU_TYPE = os.environ.get("GPU_TYPE", "l40s")
 GPU_COUNT = os.environ.get("GPU_COUNT", 1)
 
 GPU_CONFIG = f"{GPU_TYPE}:{GPU_COUNT}"
@@ -41,13 +41,13 @@
 
 MINUTES = 60  # seconds
 
-# We use a [LLaVA-NeXT](https://huggingface.co/docs/transformers/en/model_doc/llava_next)
-# model built on top of Meta's LLaMA 3 8B.
+# We use the [Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct)
+# model by Alibaba.
 
-MODEL_PATH = "lmms-lab/llama3-llava-next-8b"
-MODEL_REVISION = "e7e6a9fd5fd75d44b32987cba51c123338edbede"
-TOKENIZER_PATH = "lmms-lab/llama3-llava-next-8b-tokenizer"
-MODEL_CHAT_TEMPLATE = "llama-3-instruct"
+MODEL_PATH = "Qwen/Qwen2-VL-7B-Instruct"
+MODEL_REVISION = "a7a06a1cc11b4514ce9edcde0e3ca1d16e5ff2fc"
+TOKENIZER_PATH = "Qwen/Qwen2-VL-7B-Instruct"
+MODEL_CHAT_TEMPLATE = "qwen2-vl"
 
 # We download it from the Hugging Face Hub using the Python function below.
 
@@ -73,12 +73,15 @@ def download_model_to_image():
 vlm_image = (
     modal.Image.debian_slim(python_version="3.11")
     .pip_install(  # add sglang and some Python dependencies
-        "sglang[all]==0.1.17",
-        "transformers==4.40.2",
+        "transformers==4.47.1",
         "numpy<2",
         "fastapi[standard]==0.115.4",
         "pydantic==2.9.2",
         "starlette==0.41.2",
+        "torch==2.4.0",
+        "sglang[all]==0.4.1",
+        # as per sglang website: https://sgl-project.github.io/start/install.html
+        extra_options="--find-links https://flashinfer.ai/whl/cu124/torch2.4/flashinfer/",
     )
     .run_function(  # download the model by running a Python function
         download_model_to_image
@@ -94,11 +97,11 @@ def download_model_to_image():
 
 # The code below adds a modal `Cls` to an `App` that runs the VLM.
 
-# We define a method `generate` that takes a URL for an image URL and a question
+# We define a method `generate` that takes a URL for an image and a question
 # about the image as inputs and returns the VLM's answer.
 
 # By decorating it with `@modal.web_endpoint`, we expose it as an HTTP endpoint,
-# so it can be accessed over the public internet from any client.
+# so it can be accessed over the public Internet from any client.
 
 app = modal.App("example-sgl-vlm")
 
@@ -129,6 +132,8 @@ def start_runtime(self):
 
     @modal.web_endpoint(method="POST", docs=True)
     def generate(self, request: dict):
+        from pathlib import Path
+
         import sglang as sgl
         from term_image.image import from_file
 
@@ -140,18 +145,16 @@ def generate(self, request: dict):
         if image_url is None:
             image_url = "https://modal-public-assets.s3.amazonaws.com/golden-gate-bridge.jpg"
 
-        image_filename = image_url.split("/")[-1]
-        image_path = f"/tmp/{uuid4()}-{image_filename}"
         response = requests.get(image_url)
-
         response.raise_for_status()
 
-        with open(image_path, "wb") as file:
-            file.write(response.content)
+        image_filename = image_url.split("/")[-1]
+        image_path = Path(f"/tmp/{uuid4()}-{image_filename}")
+        image_path.write_bytes(response.content)
 
         @sgl.function
         def image_qa(s, image_path, question):
-            s += sgl.user(sgl.image(image_path) + question)
+            s += sgl.user(sgl.image(str(image_path)) + question)
             s += sgl.assistant(sgl.gen("answer"))
 
         question = request.get("question")