# Snapshot GPU memory to speed up cold starts | Modal Docs

- **URL:** https://modal.com/docs/examples/gpu_snapshot
- **Summary:** This example demonstrates how to use GPU memory snapshots to speed up model loading. Note that GPU memory snapshotting is an experimental feature, so test carefully before using in production! You can read more about GPU memory snapshotting, and its caveats, here.

[View on GitHub](https://github.com/modal-labs/modal-examples/blob/main/06_gpu_and_ml/gpu_snapshot.py)

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Snapshot GPU memory to speed up cold starts

This example demonstrates how to use GPU memory snapshots to speed up model loading. Note that GPU memory snapshotting is an experimental feature, so test carefully before using in production! You can read more about GPU memory snapshotting, and its caveats, [here](https://modal.com/docs/guide/memory-snapshot)
.

GPU snapshots can only be used with deployed Functions, so first deploy the App:

    modal deploy -m 06_gpu_and_ml.gpu_snapshot

Next, invoke the Function:

    python -m 06_gpu_and_ml.gpu_snapshot

The full code is below:

    import modal
    
    image = modal.Image.debian_slim().uv_pip_install("sentence-transformers<6")
    app_name = "example-gpu-snapshot"
    app = modal.App(app_name, image=image)
    
    snapshot_key = "v1"  # change this to invalidate the snapshot cache
    
    with image.imports():  # import in the global scope so imports can be snapshot
        from sentence_transformers import SentenceTransformer
    
    
    @app.cls(
        gpu="a10",
        enable_memory_snapshot=True,
        experimental_options={"enable_gpu_snapshot": True},
    )
    class SnapshotEmbedder:
        @modal.enter(snap=True)
        def load(self):
            # during enter phase of container lifecycle,
            # load the model onto the GPU so it can be snapshot
            print("loading model")
            self.model = SentenceTransformer("BAAI/bge-small-en-v1.5", device="cuda")
            print(f"snapshotting {snapshot_key}")
    
        @modal.method()
        def run(self, sentences: list[str]) -> list[list[float]]:
            # later invocations of the Function will start here
            embeddings = self.model.encode(sentences, normalize_embeddings=True)
            return embeddings.tolist()
    
    
    if __name__ == "__main__":
        # after deployment, we can use the class from anywhere
        SnapshotEmbedder = modal.Cls.from_name(app_name, "SnapshotEmbedder")
        embedder = SnapshotEmbedder()
        try:
            print("calling Modal Function")
            print(embedder.run.remote(sentences=["what is the meaning of life?"]))
        except modal.exception.NotFoundError:
            raise Exception(
                f"To take advantage of GPU snapshots, deploy first with modal deploy {__file__}"
            )

[Snapshot GPU memory to speed up cold starts](https://modal.com/docs/examples/gpu_snapshot#snapshot-gpu-memory-to-speed-up-cold-starts)
> Common reference appendix (shared error/status catalog): see [../_shared-appendix.md](../_shared-appendix.md).
    python 06_gpu_and_ml/gpu_snapshot.py