diff --git a/06_gpu_and_ml/mlc_inference.py b/06_gpu_and_ml/mlc_inference.py
deleted file mode 100644
index 574585bc8..000000000
--- a/06_gpu_and_ml/mlc_inference.py
+++ /dev/null
@@ -1,166 +0,0 @@
-# ---
-# lambda-test: false
-# ---
-# # Llama 2 inference with MLC
-#
-# [Machine Learning Compilation (MLC)](https://mlc.ai/mlc-llm/) is high-performance tool for serving
-# LLMs including Llama 2. We will use the [`mlc_chat`](https://mlc.ai/mlc-llm/docs/index.html) package
-# and the pre-compiled Llama 2 binaries to run inference using a Modal GPU.
-#
-# This example is adapted from this [MLC chat collab](https://colab.research.google.com/github/mlc-ai/notebooks/blob/main/mlc-llm/tutorial_chat_module_getting_started.ipynb#scrollTo=yYwjsCOK7Jij).
-import queue
-import threading
-import time
-from typing import Dict, Generator, List
-
-import modal
-
-# ## Imports and global settings
-#
-# Determine which [GPU](https://modal.com/docs/guide/gpu#gpu-acceleration) you want to use.
-GPU: str = "a10g"
-
-# Chose model size. At the time of writing MLC chat only
-# provides compiled binaries for Llama 7b and 13b.
-LLAMA_MODEL_SIZE: str = "13b"
-
-# Define the image and [Modal Stub](https://modal.com/docs/reference/modal.Stub#modalstub).
-# We use an [official NVIDIA CUDA 12.2 image](https://hub.docker.com/r/nvidia/cuda)
-# to match MLC CUDA requirements.
-mlc_image = (
-    modal.Image.from_registry(
-        "nvidia/cuda:12.2.2-cudnn8-runtime-ubuntu22.04",
-        add_python="3.11",
-    ).run_commands(
-        "apt-get update",
-        "apt-get install -y curl git",
-        # Install git lfs
-        "curl -sSf https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | bash",
-        "apt-get install -y git-lfs",
-        "pip3 install --pre --force-reinstall mlc-ai-nightly-cu122 mlc-chat-nightly-cu122 -f https://mlc.ai/wheels",
-    )
-    # "These commands will download many prebuilt libraries as well as the chat
-    # configuration for Llama-2-7b that mlc_chat needs" [...]
-    .run_commands(
-        "mkdir -p dist/prebuilt",
-        "git clone https://github.com/mlc-ai/binary-mlc-llm-libs.git dist/prebuilt/lib",
-        f"cd dist/prebuilt && git clone https://huggingface.co/mlc-ai/mlc-chat-Llama-2-{LLAMA_MODEL_SIZE}-chat-hf-q4f16_1",
-    )
-)
-stub = modal.Stub("mlc-inference")
-
-
-LOADING_MESSAGE: str = f"""
-
-                      #%%%%%%%%%%%%(         #%%%%%%%%%%%%#
-                    ,%##%,         %%      .%#(%/         %%.
-                   %%.  .%#         (%*   (%*   %%         *%/
-                 .%#      %%.        .%% %%      (%*         %%
-                #%,        ,%%%%%%%%%%%%%/        .%%         (%*
-               %%         (%*         %%*%(         #%,         %%
-             (%*         %%         *%(   %%         .%#         #%,
-            %%         *%/         %%.     *%(         #%,        .%#
-          /%(         %%         .%#         %%         .%%%%%%%%%%%%%.
-           (%/      ,%#         #%,           /%(      .%%         (%,
-             %%    %%.        .%%               %%    (%*         %%
-              (%*.%#         (%*                 /%/ %%         /%/
-                %%%%%%%%%%%%%%                     %%%%%%%%%%%%%%
-
-                      LOADING => Llama 2 ({LLAMA_MODEL_SIZE}) [{GPU}]
-
-
-"""
-
-
-# ## Define Modal function
-#
-# The `generate` function will load MLC chat and the compiled model into
-# memory and run inference on an input prompt. This is a generator, streaming
-# tokens back to the client as they are generated.
-@stub.function(gpu=GPU, image=mlc_image)
-def generate(prompt: str) -> Generator[Dict[str, str], None, None]:
-    from mlc_chat import ChatModule
-    from mlc_chat.callback import DeltaCallback
-
-    yield {
-        "type": "loading",
-        "message": LOADING_MESSAGE + "\n\n",
-    }
-
-    class QueueCallback(DeltaCallback):
-        """Stream the output of the chat module to client."""
-
-        def __init__(self, callback_interval: float):
-            super().__init__()
-            self.queue: queue.Queue = queue.Queue()
-            self.stopped = False
-            self.callback_interval = callback_interval
-
-        def delta_callback(self, delta_message: str):
-            self.stopped = False
-            self.queue.put(delta_message)
-
-        def stopped_callback(self):
-            self.stopped = True
-
-    cm = ChatModule(
-        model=f"/dist/prebuilt/mlc-chat-Llama-2-{LLAMA_MODEL_SIZE}-chat-hf-q4f16_1",
-        model_lib_path=f"/dist/prebuilt/lib/Llama-2-{LLAMA_MODEL_SIZE}-chat-hf-q4f16_1-cuda.so",
-    )
-    queue_callback = QueueCallback(callback_interval=1)
-
-    # Generate tokens in a background thread so we can yield tokens
-    # to caller as a generator.
-    def _generate():
-        cm.generate(
-            prompt=prompt,
-            progress_callback=queue_callback,
-        )
-
-    background_thread = threading.Thread(target=_generate)
-    background_thread.start()
-
-    # Yield as a generator to caller function and spawn
-    # text-to-speech functions.
-    while not queue_callback.stopped:
-        yield {"type": "output", "message": queue_callback.queue.get()}
-
-
-# ## Run model
-#
-# Create a local Modal entrypoint that calls the `generate` function.
-# This uses the `curses` to render tokens as they are streamed back
-# from Modal.
-#
-# Run this locally with `modal run -q mlc_inference.py --prompt "What is serverless computing?"`
-@stub.local_entrypoint()
-def main(prompt: str):
-    import curses
-
-    def _generate(stdscr):
-        buffer: List[str] = []
-
-        def _buffered_message():
-            return "".join(buffer) + ("\n" * 4)
-
-        start = time.time()
-        for payload in generate.remote_gen(prompt):
-            message = payload["message"]
-            if payload["type"] == "loading":
-                stdscr.clear()
-                stdscr.addstr(0, 0, message)
-                stdscr.refresh()
-            else:
-                buffer.append(message)
-                stdscr.clear()
-                stdscr.addstr(0, 0, _buffered_message())
-                stdscr.refresh()
-
-        n_tokens = len(buffer)
-        elapsed = time.time() - start
-        print(
-            f"[DONE] {n_tokens} tokens generated in {elapsed:.2f}s ({n_tokens / elapsed:.0f} tok/s). Press any key to exit."
-        )
-        stdscr.getch()
-
-    curses.wrapper(_generate)