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+
+# vLLM
+
+[vLLM](https://github.com/vllm-project/vllm) is a high-throughput inference engine for serving LLMs at scale. It continuously batches requests and keeps KV cache memory compact with PagedAttention.
+
+Set `model_impl="transformers"` to load a model using the Transformers modeling backend.
+
+```py
+from vllm import LLM
+
+llm = LLM(model="meta-llama/Llama-3.2-1B", model_impl="transformers")
+print(llm.generate(["The capital of France is"]))
+```
+
+Pass `--model-impl transformers` to the `vllm serve` command for online serving.
+
+```bash
+vllm serve meta-llama/Llama-3.2-1B \
+    --task generate \
+    --model-impl transformers
+```
+
+## Transformers integration
+
+1. [`AutoConfig.from_pretrained`] loads the model's `config.json` from the Hub or your Hugging Face cache. vLLM checks the `architectures` field against its internal model registry to determine which vLLM model class to use.
+2. If the model isn't in the registry, vLLM calls [`AutoModel.from_config`] to load the Transformers model implementation instead.
+3. [`AutoTokenizer.from_pretrained`] loads the tokenizer files. vLLM caches some tokenizer internals to reduce overhead during inference.
+4. Model weights download from the Hub in safetensors format.
+
+Setting `model_impl="transformers"` bypasses the vLLM model registry and loads directly from Transformers. vLLM replaces most model modules (MoE, attention, linear layers) with its own optimized versions while keeping the Transformers model structure.
+
+## Resources
+
+- [vLLM docs](https://docs.vllm.ai/en/latest/models/supported_models.html#transformers) for more usage examples and tips.
+- [Integration with Hugging Face](https://docs.vllm.ai/en/latest/design/huggingface_integration/) explains how vLLM integrates with Transformers.