Integrate vLLM with OnPremiseAgent for high-throughput, low-latency model serving in production environments. vLLM's PagedAttention algorithm delivers up to 24x higher throughput than standard serving frameworks, making it ideal for enterprise workloads with multiple concurrent agents.
API Key, Token
AI Runtimes
vLLM 0.4+, NVIDIA A100/H100/L40S, CUDA 11.8+
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Everything you need to integrate vLLM into your on-premise agent workflows.
vLLM's PagedAttention algorithm enables up to 24x higher throughput than naive serving approaches.
Drop-in replacement for OpenAI API with the same request/response format.
Serve multiple models from a single vLLM instance with automatic routing.
Run quantized models (GPTQ, AWQ, SqueezeLLM) for reduced memory footprint.
Install vLLM on your GPU server: pip install vllm. Requires NVIDIA GPU with CUDA 11.8+.
Handle thousands of concurrent agent requests with continuous batching and PagedAttention.
Switch from cloud APIs to on-premise serving with zero code changes using OpenAI-compatible endpoint.
Reduce inference costs by 90%+ by serving models on your own GPU infrastructure.
vLLM supports NVIDIA GPUs with compute capability 7.0+ (V100, A100, H100, L40S, RTX 3090/4090).
Yes. vLLM provides an OpenAI-compatible API server that works as a drop-in replacement.
Combine vLLM with these connectors for a complete integration stack.
Deploy on your own infrastructure with full data sovereignty. Get started in minutes.