transformers vs vllm
A neutral, constraint-first comparison - live GitHub stats and typed relationships, not marketing.
| transformers | vllm | |
|---|---|---|
| Tagline | 🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, | A high-throughput and memory-efficient inference and serving engine for LLMs |
| Stars | 162k | 86k |
| Forks | 34k | 19k |
| Open issues | 2.5k | 5.6k |
| Language | Python | Python |
| License | Apache-2.0 | Apache-2.0 |
| Last pushed | Jul 7, 2026 | Jul 7, 2026 |
| Categories | Model Training, Inference & Serving, LLM Frameworks | Inference & Serving |
transformers
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models,
Python
vllm
vLLM is a fast and easy-to-use library designed for efficient Large Language Model (LLM) inference and serving. It includes state-of-the-art performance with features like PagedAttention, quantization support, optimized attention kernels, and seamless integration with Hugging Face models.
Python