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41 lines
1.7 KiB
Markdown
41 lines
1.7 KiB
Markdown
---
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title: Offline Inference
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---
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[](){ #offline-inference }
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Offline inference is possible in your own code using vLLM's [`LLM`][vllm.LLM] class.
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For example, the following code downloads the [`facebook/opt-125m`](https://huggingface.co/facebook/opt-125m) model from HuggingFace
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and runs it in vLLM using the default configuration.
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```python
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from vllm import LLM
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# Initialize the vLLM engine.
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llm = LLM(model="facebook/opt-125m")
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```
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After initializing the `LLM` instance, use the available APIs to perform model inference.
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The available APIs depend on the model type:
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- [Generative models][generative-models] output logprobs which are sampled from to obtain the final output text.
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- [Pooling models][pooling-models] output their hidden states directly.
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!!! info
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[API Reference][offline-inference-api]
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### Ray Data LLM API
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Ray Data LLM is an alternative offline inference API that uses vLLM as the underlying engine.
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This API adds several batteries-included capabilities that simplify large-scale, GPU-efficient inference:
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- Streaming execution processes datasets that exceed aggregate cluster memory.
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- Automatic sharding, load balancing, and autoscaling distribute work across a Ray cluster with built-in fault tolerance.
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- Continuous batching keeps vLLM replicas saturated and maximizes GPU utilization.
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- Transparent support for tensor and pipeline parallelism enables efficient multi-GPU inference.
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The following example shows how to run batched inference with Ray Data and vLLM:
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<gh-file:examples/offline_inference/batch_llm_inference.py>
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For more information about the Ray Data LLM API, see the [Ray Data LLM documentation](https://docs.ray.io/en/latest/data/working-with-llms.html).
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