Summary
Multi-model inference is the practice of running multiple specialized AI models (embedding, reranking, OCR, vision, extraction, generation) in a single pipeline, ideally sharing GPU resources to minimize cost and latency.
The Problem
Standard serving tools (vLLM, TEI) each serve one model type and assume exclusive GPU ownership:
- vLLM serves LLMs, occupies ~90% of GPU at startup
- TEI serves embedding/reranking models
- Neither covers OCR, vision, or document parsing
- Result: 4 servers + 4 APIs + 4 deployments, each with dedicated GPU
Why GPU Sharing Matters
- Small specialized models are accurate enough for their tasks
- But switching to small models doesn’t make inference cheaper by itself
- Savings only appear when models share GPUs
- Requires a single engine that can run all model types
Solutions
- superlinked-inference-engine — Open-source engine running all model types on shared GPUs
- Smart batching by compute cost (not item count)
- Dynamic model loading/eviction (LRU cache pattern)
- Gateway-based shared queue across workers
Related Concepts
- harness — The program that runs LLMs
- inference — General inference optimization
- rag — Retrieval-augmented generation pipelines
Evidence Timeline
- 2026-07-28: Avi Chawla’s article detailed multi-model inference challenges and SIE solution (sources/articles/avichawla-superlinked-sie-2026.md)