vllm/docs/models/extensions/tensorizer.md

# Loading models with CoreWeave's Tensorizer

vLLM supports loading models with [CoreWeave's Tensorizer](https://docs.coreweave.com/coreweave-machine-learning-and-ai/inference/tensorizer).
vLLM model tensors that have been serialized to disk, an HTTP/HTTPS endpoint, or S3 endpoint can be deserialized
at runtime extremely quickly directly to the GPU, resulting in significantly
shorter Pod startup times and CPU memory usage. Tensor encryption is also supported.

vLLM fully integrates Tensorizer in to its model loading machinery. The following will give a brief overview on how to get started with using Tensorizer on vLLM.

## Installing Tensorizer

To install `tensorizer`, run `pip install vllm[tensorizer]`.

## The basics

To load a model using Tensorizer, the model first needs to be serialized by
Tensorizer. [The example script](../../examples/others/tensorize_vllm_model.md) takes care of this process.

Let's walk through a basic example by serializing `facebook/opt-125m` using the script, and then loading it for inference.

## Serializing a vLLM model with Tensorizer

To serialize a model with Tensorizer, call the example script with the necessary
CLI arguments. The docstring for the script itself explains the CLI args
and how to use it properly in great detail, and we'll use one of the examples from the docstring directly, assuming we want to serialize and save our model at our S3 bucket example `s3://my-bucket`:

```bash
python examples/others/tensorize_vllm_model.py \
   --model facebook/opt-125m \
   serialize \
   --serialized-directory s3://my-bucket \
   --suffix v1
```

This saves the model tensors at `s3://my-bucket/vllm/facebook/opt-125m/v1`. If you intend on applying a LoRA adapter to your tensorized model, you can pass the HF id of the LoRA adapter in the above command, and the artifacts will be saved there too:

```bash
python examples/others/tensorize_vllm_model.py \
   --model facebook/opt-125m \
   --lora-path <lora_id> \
   serialize \
   --serialized-directory s3://my-bucket \
   --suffix v1
```

## Serving the model using Tensorizer

Once the model is serialized where you want it, you can load the model using `vllm serve` or the `LLM` entrypoint. You can pass the directory where you saved the model to the `model` argument for `LLM()` and `vllm serve`. For example, to serve the tensorized model saved previously with the LoRA adapter, you'd do:

```bash
vllm serve s3://my-bucket/vllm/facebook/opt-125m/v1 \
    --load-format tensorizer \
    --enable-lora 
```

Or, with `LLM()`:

```python
from vllm import LLM
llm = LLM(
    "s3://my-bucket/vllm/facebook/opt-125m/v1", 
    load_format="tensorizer",
    enable_lora=True,
)
```

## Options for configuring Tensorizer

`tensorizer`'s core objects that serialize and deserialize models are `TensorSerializer` and `TensorDeserializer` respectively. In order to pass arbitrary kwargs to these, which will configure the serialization and deserialization processes, you can provide them as keys to `model_loader_extra_config` with `serialization_kwargs` and `deserialization_kwargs` respectively. Full docstrings detailing all parameters for the aforementioned objects can be found in `tensorizer`'s [serialization.py](https://github.com/coreweave/tensorizer/blob/main/tensorizer/serialization.py) file.

As an example, CPU concurrency can be limited when serializing with `tensorizer` via the `limit_cpu_concurrency` parameter in the initializer for `TensorSerializer`. To set `limit_cpu_concurrency` to some arbitrary value, you would do so like this when serializing:

```bash
python examples/others/tensorize_vllm_model.py \
   --model facebook/opt-125m \
   --lora-path <lora_id> \
   serialize \
   --serialized-directory s3://my-bucket \
   --serialization-kwargs '{"limit_cpu_concurrency": 2}' \
   --suffix v1
```

As an example when customizing the loading process via `TensorDeserializer`, you could limit the number of concurrency readers during deserialization with the `num_readers` parameter in the initializer via `model_loader_extra_config` like so:

```bash
vllm serve s3://my-bucket/vllm/facebook/opt-125m/v1 \
    --load-format tensorizer \
    --enable-lora \
    --model-loader-extra-config '{"deserialization_kwargs": {"num_readers": 2}}'
```

Or with `LLM()`:

```python
from vllm import LLM
llm = LLM(
    "s3://my-bucket/vllm/facebook/opt-125m/v1", 
    load_format="tensorizer",
    enable_lora=True,
    model_loader_extra_config={"deserialization_kwargs": {"num_readers": 2}},
)
```