superlinked/sie
Open-source inference server and production cluster for all the models your agent needs. observed · 2026-08-28
Health v2 · maintenance only
92/100
- Activity 98
- Release rhythm 96
- Longevity 73
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: 1
- age_days: 1030
- days_rel: 24
- days_push: 12
- n_releases_24m: 46
Adoption not part of the score
2830 stars · 280 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
SIE (Superlinked Inference Engine) is an open-source, self-hosted inference server and production cluster that serves 100+ open models (embedders, rerankers, extractors, LLMs) behind one OpenAI-compatible API. It handles multi-model GPU sharing, on-demand model loading, and ships a full production stack with KEDA autoscaling and Terraform for GKE, EKS, and AKS.
Use cases
- self-host embeddings and rerankers for RAG
- run open LLM inference in my own cloud
- replace per-token embedding API costs with self-hosted models
- serve many small ML models on one GPU
- add semantic search embeddings to Qdrant or Weaviate
- deploy an OpenAI-compatible inference endpoint on Kubernetes
- extract entities and OCR documents for agent pipelines
- air-gapped inference for agents
When to choose
- You need self-hosted, privacy-preserving inference for embeddings, reranking, extraction, and small LLMs behind one API
- You run many small models on shared GPUs and need fast model switching
- You want OpenAI-compatible endpoints for drop-in migration from hosted APIs
- You need production deployment with autoscaling on GKE, EKS, or AKS
When to avoid
- You only need to serve one very large model across many GPUs - tools like vLLM or SGLang alone fit better
- You want a fully managed hosted API with no infrastructure to run
- You need non-Python/TypeScript SDKs or integrations beyond the supported vector stores and agent frameworks
Facets
service · maturity active
llm-inference rag machine-learning nlp search-engine ocr api-framework http-server gpu-computing sdk machine-learning large-language-models self-hosted developer-tools python self-hosted cloud cross-platform inference-server embeddings reranking vector-search openai-compatible-api model-serving rag-pipeline gpu-sharing semantic-search splade colbert mlops retrieval-augmented-generation natural-language-processing search ai-agents docker kubernetes gpu linux macos
10 sources
- readme: https://github.com/superlinked/sie · fetched 2026-08-28 · e4a4011b2872
- homepage: https://superlinked.com · fetched 2026-08-29 · 3526d02626b4
- site_page: https://superlinked.com/docs/examples · fetched 2026-08-29 · 9c55e0c185c2
- site_page: https://superlinked.com/docs · fetched 2026-08-29 · 9d6306f6630a
- site_page: https://superlinked.com/docs/integrations/chroma · fetched 2026-08-29 · ef8d5ea10658
- site_page: https://superlinked.com/docs/integrations/lancedb · fetched 2026-08-29 · 9a7bc0ad6ee7
- site_page: https://superlinked.com/docs/integrations/qdrant · fetched 2026-08-29 · ad712eace48a
- site_page: https://superlinked.com/docs/integrations/weaviate · fetched 2026-08-29 · b368fdba0731
- site_page: https://superlinked.com/docs/deployment · fetched 2026-08-29 · 0c259de0a596
- site_page: https://superlinked.com/docs/deployment/cloud-aws · fetched 2026-08-29 · f74823ceebea
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| superlinked/sie | main | 92 |
For agents
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem