text: Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsAI21Aleph AlphaAmazon API GatewayAnyscaleAzure OpenAIAzureML Online EndpointBananaBasetenBeamBedrockCerebriumAIChatGLMClarifaiCohereC TransformersDatabricksDeepInfraForefrontAIGoogle Cloud Platform Vertex AI PaLMGooseAIGPT4AllHugging Face HubHugging Face Local PipelinesHuggingface TextGen InferenceJSONFormerKoboldAI APILlama-cppCaching integrationsManifestModalMosaicMLNLP CloudoctoaiOpenAIOpenLLMOpenLMPetalsPipelineAIPredibasePrediction GuardPromptLayer OpenAIRELLMReplicateRunhouseSageMakerEndpointStochasticAITextGenTongyi QwenWriterMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerIntegrationsLLMsRunhouseRunhouseThe Runhouse allows remote compute and data across environments and users. See the Runhouse docs.This example goes over how to use LangChain and Runhouse to interact with models hosted on your own GPU, or on-demand GPUs on AWS, GCP, AWS, or Lambda.Note: Code uses SelfHosted name instead of the Runhouse.pip install runhousefrom langchain.llms import SelfHostedPipeline, SelfHostedHuggingFaceLLMfrom langchain import PromptTemplate, LLMChainimport runhouse as rh    INFO | 2023-04-17 16:47:36,173 | No auth token provided, so not using RNS API to save and load configs# For an on-demand A100 with GCP, Azure, or Lambdagpu = rh.cluster(name="rh-a10x",