vLLM is a production-grade LLM inference server. PagedAttention, continuous batching, and tensor parallelism deliver 10–24x higher throughput than naive HuggingFace inference. OpenAI-compatible API. GPU required.
Ideal for serving 7B–13B models in production
See matching serversRecommended — serve 70B models at production scale
See matching serversTensor parallelism across multiple GPUs
See matching serversLooking for a specific GPU configuration?
Browse all GPU dedicated server plans →vLLM's PagedAttention manages GPU memory like virtual memory in an OS, allowing efficient KV cache reuse. This delivers 10–24x higher throughput than running models directly with HuggingFace Transformers.
vLLM exposes an OpenAI-compatible API. Change one environment variable in your application (the base URL) and your app runs against your own model instead of paying per token.
Llama 3, Mistral, Mixtral, Qwen, DeepSeek, Gemma — vLLM supports all major model architectures. Pull any model from HuggingFace Hub and serve it with vLLM without code changes.
High-throughput LLM serving generates significant outbound traffic. Bandwidth caps will limit your API throughput and add unpredictable costs. All Dedimax plans include unlimited traffic.
vLLM is the leading open-source LLM inference framework for production deployments. Its PagedAttention memory management and continuous batching deliver 10–24x higher throughput compared to naive inference, making it the choice for teams that need to serve LLMs at scale. vLLM exposes an OpenAI-compatible API — existing applications that call GPT-4 can switch to your self-hosted model by changing a single URL. For 7–13B models, an RTX 4090 with 24 GB VRAM provides a cost-effective starting point. For 70B models and production traffic, an A100 with 80 GB VRAM is the standard deployment target.
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