diff --git a/06_gpu_and_ml/embeddings/text_embeddings_inference.py b/06_gpu_and_ml/embeddings/text_embeddings_inference.py
new file mode 100644
index 000000000..e07b8bda1
--- /dev/null
+++ b/06_gpu_and_ml/embeddings/text_embeddings_inference.py
@@ -0,0 +1,145 @@
+import subprocess
+from pathlib import Path
+
+from modal import Image, Secret, Stub, Volume, gpu, method
+
+GPU_CONFIG = gpu.A10G()
+MODEL_ID = "BAAI/bge-base-en-v1.5"
+BATCH_SIZE = 32
+DOCKER_IMAGE = (
+    "ghcr.io/huggingface/text-embeddings-inference:86-0.4.0"  # Ampere 86 for A10s.
+    # "ghcr.io/huggingface/text-embeddings-inference:0.4.0" # Ampere 80 for A100s.
+    # "ghcr.io/huggingface/text-embeddings-inference:0.3.0"  # Turing for T4s.
+)
+
+DATA_PATH = Path("/data/dataset.jsonl")
+
+LAUNCH_FLAGS = [
+    "--model-id",
+    MODEL_ID,
+    "--port",
+    "8000",
+]
+
+
+def spawn_server() -> subprocess.Popen:
+    import socket
+
+    process = subprocess.Popen(["text-embeddings-router"] + LAUNCH_FLAGS)
+
+    # Poll until webserver at 127.0.0.1:8000 accepts connections before running inputs.
+    while True:
+        try:
+            socket.create_connection(("127.0.0.1", 8000), timeout=1).close()
+            print("Webserver ready!")
+            return process
+        except (socket.timeout, ConnectionRefusedError):
+            # Check if launcher webserving process has exited.
+            # If so, a connection can never be made.
+            retcode = process.poll()
+            if retcode is not None:
+                raise RuntimeError(
+                    f"launcher exited unexpectedly with code {retcode}"
+                )
+
+
+def download_model():
+    # Wait for server to start. This downloads the model weights when not present.
+    spawn_server()
+
+
+image = (
+    Image.from_registry(
+        "ghcr.io/huggingface/text-embeddings-inference:86-0.4.0",
+        add_python="3.10",
+    )
+    .dockerfile_commands("ENTRYPOINT []")
+    .run_function(download_model, gpu=GPU_CONFIG)
+    .pip_install("httpx")
+)
+
+volume = Volume.persisted("tei-hn-data")
+
+stub = Stub("example-tei", image=image)
+
+
+@stub.cls(
+    secret=Secret.from_name("huggingface"),
+    gpu=GPU_CONFIG,
+    allow_concurrent_inputs=10,
+    concurrency_limit=10,
+)
+class TextEmbeddingsInference:
+    def __enter__(self):
+        from httpx import AsyncClient
+
+        self.process = spawn_server()
+        self.client = AsyncClient(base_url="http://127.0.0.1:8000")
+
+    def __exit__(self, _exc_type, _exc_value, _traceback):
+        self.process.terminate()
+
+    @method()
+    async def embed(self, id: str, input: str):
+        resp = self.client.post("/embed", json={"inputs": [input]})
+        resp = await resp
+        resp.raise_for_status()
+        outputs = resp.json()
+        return id, outputs[0]
+
+
+def download_data():
+    import json
+    import os
+
+    from google.cloud import bigquery
+    from google.oauth2 import service_account
+
+    service_account_info = json.loads(os.environ["SERVICE_ACCOUNT_JSON"])
+    credentials = service_account.Credentials.from_service_account_info(
+        service_account_info
+    )
+
+    client = bigquery.Client(credentials=credentials)
+
+    iterator = client.list_rows(
+        "bigquery-public-data.hacker_news.full",
+        max_results=100_000,
+    )
+    df = iterator.to_dataframe(progress_bar_type="tqdm")
+    df["id"] = df["id"].astype(int)
+    # TODO: better chunking / splitting.
+    df["text"] = df["text"].apply(lambda x: x[:512])
+
+    data = list(zip(df["id"], df["text"]))
+
+    with open(DATA_PATH, "w") as f:
+        json.dump(data, f)
+
+    volume.commit()
+
+
+@stub.function(
+    image=Image.debian_slim().pip_install(
+        "google-cloud-bigquery", "pandas", "db-dtypes", "tqdm"
+    ),
+    secrets=[Secret.from_name("bigquery")],
+    volumes={DATA_PATH.parent: volume},
+)
+def embed_dataset():
+    import json
+
+    model = TextEmbeddingsInference()
+
+    if not DATA_PATH.exists():
+        print("Downloading data...")
+        download_data()
+
+    with open(DATA_PATH) as f:
+        data = json.loads(f.read())
+
+    # data is of type list[tuple[str, str]].
+    # starmap spreads the tuples into positional arguments.
+    output = list(model.embed.starmap(data))
+
+    print(len(output))