LMDeploy
ML inferencelmdeploy
InternLM toolkit that compresses and serves LLMs behind an OpenAI-compatible API server.
- Provider
- default
- Kind
- ML inference
- Category
- Compute
| property | type | values | required | kind default | this component |
|---|---|---|---|---|---|
| accelerator | enum | none | gpu | asic | no | none | ▸ gpu |
| acceleratorModel | enum | nvidia-h100 | nvidia-h200 | nvidia-b200 | nvidia-l4 | nvidia-a100 | amd-mi300x | aws-trainium | aws-inferentia | google-tpu | intel-gaudi | groq-lpu | cerebras-wse | no | — | inherited |
| apiKeyRequired | boolean | — | no | false | inherited |
| backend | enum | turbomind | pytorch | no | turbomind | inherited |
| embeddingSyncMinutes | integer | — | no | — | inherited |
| inputs | enum | live | historical | both | no | live | ▸ live |
| instances | integer | — | no | — | inherited |
| managed | boolean | — | no | false | ▸ false |
| port | integer | — | no | 23333 | inherited |
| snapshotToObjectStore | boolean | — | no | false | inherited |
| tensorParallelSize | integer | — | no | 1 | inherited |
| training | enum | batch | online | no | batch | ▸ batch |
Every rule whose selector covers this component's kind or its category. Whether a rule fires depends on the design: its properties, its edges and its groups.
stateful-node-without-zone-redundancy
A stateful compute node pinned to exactly one availability zone disappears with that zone, and its state goes with it. Nodes inside a cell are exempt, because there the cell is deliberately single-zone and redundancy comes from the other cells.
- — all of these hold:
- — the node's `stateless` is false
- — the node sits inside exactly 1 availability-zone group
- — this does not hold:
- — the node sits inside at least one cell group
nodes:
- { id: "lmdeploy", component: "lmdeploy" }SageMaker endpointsagemaker-endpointAzure ML endpointazure-ml-endpointWorkers AIcloudflare-workers-aiAnyscale Endpointsanyscale-endpointsBananabanana-devBasetenbasetenBeambeam-cloudBentoMLbentomlHugging Face Inference Endpointshuggingface-inferenceInfinityinfinity-embeddingsJanjanKServekservellama.cpp serverllama-cpp-serverllm-dllm-dLM Studiolm-studioLocalAIlocalaiGeneric ML inferenceml-inferenceMLC LLMmlc-llmModalmodalMystic AImystic-aiNVIDIA NIM (self-hosted)nvidia-nimOctoAIoctoaiOllamaollamaRay Serveray-serveSeldon Coreseldon-coreSGLangsglangTensorFlow Servingtensorflow-servingTensorRT-LLMtensorrt-llmText Embeddings Inferencetext-embeddings-inferenceText Generation Inferencetext-generation-inferenceTorchServetorchserveNVIDIA TritontritonvLLMvllmXinferencexinferenceVertex AI endpointvertex-ai-endpointOCI Data Science Model Deploymentoci-data-science-model-deploymentOCI Generative AI Dedicated AI Clusteroci-generative-ai-cluster
# yaml-language-server: $schema=../../schema/component.schema.json # LMDeploy: InternLM's toolkit for compressing and serving LLMs, with an OpenAI-compatible api_server. # Per "OpenAI Compatible Server", `lmdeploy serve api_server` listens on port 23333, runs the TurboMind engine unless # the PyTorch engine is chosen, and uses tensor parallel size 1; no API key is set unless one is passed. schemaVersion: "1" type: "component" id: "lmdeploy" kind: "ml-inference" provider: "default" docs: "https://lmdeploy.readthedocs.io/en/latest/llm/api_server.html" defaults: training: "batch" inputs: "live" accelerator: "gpu" managed: false properties: port: type: "integer" default: 23333 backend: type: "enum" values: ["turbomind", "pytorch"] default: "turbomind" tensorParallelSize: type: "integer" default: 1 apiKeyRequired: type: "boolean" default: false