2.8 KiB
(offline-inference)=
Offline Inference
You can run vLLM in your own code on a list of prompts.
The offline API is based on the {class}~vllm.LLM class.
To initialize the vLLM engine, create a new instance of LLM and specify the model to run.
For example, the following code downloads the facebook/opt-125m model from HuggingFace
and runs it in vLLM using the default configuration.
llm = LLM(model="facebook/opt-125m")
After initializing the LLM instance, you can perform model inference using various APIs.
The available APIs depend on the type of model that is being run:
- Generative models output logprobs which are sampled from to obtain the final output text.
- Pooling models output their hidden states directly.
Please refer to the above pages for more details about each API.
[API Reference](/api/offline_inference/index)
Configuration Options
This section lists the most common options for running the vLLM engine. For a full list, refer to the Engine Arguments page.
Reducing memory usage
Large models might cause your machine to run out of memory (OOM). Here are some options that help alleviate this problem.
Tensor Parallelism (TP)
Tensor parallelism (tensor_parallel_size option) can be used to split the model across multiple GPUs.
The following code splits the model across 2 GPUs.
llm = LLM(model="ibm-granite/granite-3.1-8b-instruct",
tensor_parallel_size=2)
To ensure that vLLM initializes CUDA correctly, you should avoid calling related functions (e.g. {func}`torch.cuda.set_device`)
before initializing vLLM. Otherwise, you may run into an error like `RuntimeError: Cannot re-initialize CUDA in forked subprocess`.
To control which devices are used, please instead set the `CUDA_VISIBLE_DEVICES` environment variable.
Quantization
Quantized models take less memory at the cost of lower precision.
Statically quantized models can be downloaded from HF Hub (some popular ones are available at Neural Magic) and used directly without extra configuration.
Dynamic quantization is also supported via the quantization option -- see here for more details.
Context length and batch size
You can further reduce memory usage by limiting the context length of the model (max_model_len option)
and the maximum batch size (max_num_seqs option).
llm = LLM(model="adept/fuyu-8b",
max_model_len=2048,
max_num_seqs=2)
Performance optimization and tuning
You can potentially improve the performance of vLLM by finetuning various options. Please refer to this guide for more details.