LM Studio
ML inferencelm-studio
Desktop app and headless daemon that serves local models over OpenAI-compatible endpoints.
- Provider
- default
- Kind
- ML inference
- Category
- Compute
- Official docs
- https://lmstudio.ai/docs/developer/core/server
- Also known as
- llmster
| property | type | values | required | kind default | this component |
|---|---|---|---|---|---|
| accelerator | enum | none | gpu | asic | no | none | inherited |
| 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 |
| authRequired | boolean | — | no | false | inherited |
| embeddingSyncMinutes | integer | — | no | — | inherited |
| engine | enum | llama-cpp | mlx | no | llama-cpp | inherited |
| inputs | enum | live | historical | both | no | live | ▸ live |
| instances | integer | — | no | — | inherited |
| managed | boolean | — | no | false | ▸ false |
| port | integer | — | no | 1234 | inherited |
| serveOnLocalNetwork | boolean | — | no | false | inherited |
| snapshotToObjectStore | boolean | — | no | false | 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: "lm-studio", component: "lm-studio" }SageMaker endpointsagemaker-endpointAzure ML endpointazure-ml-endpointWorkers AIcloudflare-workers-aiAnyscale Endpointsanyscale-endpointsBananabanana-devBasetenbasetenBeambeam-cloudBentoMLbentomlHugging Face Inference Endpointshuggingface-inferenceInfinityinfinity-embeddingsJanjanKServekservellama.cpp serverllama-cpp-serverllm-dllm-dLMDeploylmdeployLocalAIlocalaiGeneric 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 # LM Studio: desktop app and headless daemon (llmster) that runs local models on llama.cpp or MLX and serves them over # OpenAI-compatible endpoints. The server listens on port 1234, accepts only local connections until "Serve on Local # Network" is switched on, and asks for no API token unless authentication is turned on in the server settings. schemaVersion: "1" type: "component" id: "lm-studio" kind: "ml-inference" provider: "default" docs: "https://lmstudio.ai/docs/developer/core/server" aliases: ["llmster"] defaults: training: "batch" inputs: "live" managed: false properties: port: type: "integer" default: 1234 serveOnLocalNetwork: type: "boolean" default: false authRequired: type: "boolean" default: false engine: type: "enum" values: ["llama-cpp", "mlx"] default: "llama-cpp"