vLLM
Inference engine for large language models built around PagedAttention, which pages the attention cache to raise batch sizes and throughput. Exposes an OpenAI-compatible API.
Inference engine for large language models built around PagedAttention, which pages the attention cache to raise batch sizes and throughput. Exposes an OpenAI-compatible API.
Gateway fronting more than 100 model providers behind a single OpenAI-compatible API, adding routing rules, fallback chains, retries, per-key spend limits and request logging.
Optimized AI inference designed for production workloads that demand speed and efficiency. vLLM provides state-of-the-art inference optimization with features like continuous batching and PagedAttention, dramatically improving throughput for language models. This combo addresses the performance gap between development and production AI serving - while Ollama is perfect for experimentation, vLLM is engineered for scenarios where you need to serve many concurrent users with minimal latency. The setup includes OpenAI-compatible APIs, making it easy to switch existing applications from development to production serving without code changes.