diff --git a/text-generation-inference/tests/test_llama3_8b.py b/text-generation-inference/tests/test_llama3_8b.py
new file mode 100644
index 00000000..8efc4475
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+++ b/text-generation-inference/tests/test_llama3_8b.py
@@ -0,0 +1,81 @@
+import os
+
+import pytest
+from text_generation_server.generator import TpuGenerator
+from text_generation_server.pb.generate_pb2 import (
+    Batch,
+    NextTokenChooserParameters,
+    Request,
+    StoppingCriteriaParameters,
+)
+from tqdm import tqdm
+
+from optimum.tpu.model import fetch_model
+
+
+MODEL_ID = "meta-llama/Meta-Llama-3-8B"
+SEQUENCE_LENGTH = 256
+
+
+@pytest.fixture(scope="module")
+def model_path():
+    # Add variables to environment so they can be used in AutoModelForCausalLM
+    os.environ["HF_SEQUENCE_LENGTH"] = str(SEQUENCE_LENGTH)
+    path = fetch_model(MODEL_ID)
+    return path
+
+
+def create_request(
+    id: int,
+    inputs: str,
+    max_new_tokens=20,
+    do_sample: bool = False,
+    top_k: int = 50,
+    top_p: float = 0.9,
+    temperature: float = 1.0,
+    seed: int = 0,
+    repetition_penalty: float = 1.0,
+):
+    # For these tests we can safely set typical_p to 1.0 (default)
+    typical_p = 1.0
+    if not do_sample:
+        # Drop top_p parameter to avoid warnings
+        top_p = 1.0
+    parameters = NextTokenChooserParameters(
+        temperature=temperature,
+        top_k=top_k,
+        top_p=top_p,
+        do_sample=do_sample,
+        seed=seed,
+        repetition_penalty=repetition_penalty,
+        typical_p=typical_p,
+    )
+    stopping_parameters = StoppingCriteriaParameters(max_new_tokens=max_new_tokens)
+    return Request(id=id, inputs=inputs, parameters=parameters, stopping_parameters=stopping_parameters)
+
+
+@pytest.mark.slow
+def test_decode_single(model_path):
+    input_text = "It was a bright cold day in April, and the clocks were striking thirteen."
+    max_new_tokens = 20
+    generated_text = " Winston Smith, his chin nuzzled into his breast in an effort to escape the vile wind,"
+
+    generator = TpuGenerator.from_pretrained(
+        model_path, revision="", max_batch_size=1, max_sequence_length=SEQUENCE_LENGTH
+    )
+    request = create_request(id=0, inputs=input_text, max_new_tokens=max_new_tokens, do_sample=False)
+    batch = Batch(id=0, requests=[request], size=1, max_tokens=SEQUENCE_LENGTH)
+    generations, next_batch = generator.prefill(batch)
+    # We already generated one token: call decode max_new_tokens - 1 times
+    for _ in tqdm(range(max_new_tokens - 1)):
+        assert next_batch.size == 1
+        assert next_batch.max_tokens == 256
+        assert len(generations) == 1
+        assert len(generations[0].tokens.ids) == 1
+        generations, next_batch = generator.decode([next_batch])
+    assert next_batch is None
+    assert len(generations) == 1
+    output = generations[0].generated_text
+    assert output.generated_tokens == max_new_tokens
+    assert output.finish_reason == 0
+    assert output.text == generated_text