Ross ROSS = Recommend OSS · open-source software intelligence for agents

superlinked/sie

Open-source inference server and production cluster for all the models your agent needs. observed · 2026-08-28

github.com/superlinked/sie · homepage · Python · Apache-2.0 (permissive) 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

Full methodology

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

Member repositories

RepositoryRoleHealth v2
superlinked/siemain92

For agents

markdown · JSON · MCP: product_card(name="superlinked/sie")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem