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LMDEPLOY
Deploy and serve LLMs efficiently with optimized inference and APIs
Apache-2.0
ABOUT
Running open models in production usually means stitching together separate inference servers, quantization tools, and API wrappers while fighting GPU memory limits and latency bottlenecks. LMDeploy packages optimized runtimes, quantization, and serving interfaces so teams can turn LLMs and VLMs into deployable inference services faster.
INSTALL
pip install lmdeployINTEGRATION GUIDE
1. Serve open LLMs behind OpenAI-compatible API endpoints for existing applications
2. Quantize model weights and KV cache to reduce GPU memory requirements
3. Run batch or offline inference jobs for chat, extraction, or classification workloads
4. Deploy supported multimodal models for image-and-text inference services
TAGS
pythonllm-servingmodel-deploymentinference-optimizationquantizationopenai-compatible-apimultimodalcuda