[Hands-on] How to Serve 5 Models On One GPU
The shift from single large AI models to pipelines of specialized small models creates a hardware utilization challenge where dedicated GPUs often sit idle. Traditional serving tools like vLLM and TEI typically claim entire GPUs, preventing efficient resource sharing for sequential tasks. The Superlinked Inference Engine (SIE) addresses this by providing an open-source framework that allows multiple models to share a single GPU pool through dynamic memory management and a unified API. SIE optimizes performance by using a shared request queue and batching requests based on estimated compute costs rather than fixed counts.
閱讀原文 ↗How to serve 5 models on one GPU
The shift from single large AI models to pipelines of specialized small models creates a hardware utilization challenge where dedicated GPUs often sit idle. Traditional serving tools like vLLM and TEI typically claim entire GPUs, preventing efficient resource sharing for sequential tasks. The Superlinked Inference Engine (SIE) addresses this by providing an open-source framework that allows multiple models to share a single GPU pool through dynamic memory management and a unified API. SIE optimizes performance by using a shared request queue and batching requests based on estimated compute costs rather than fixed counts.
- Production AI systems are evolving into agentic pipelines using multiple specialized models for parsing, extraction, reranking, and generation.
- Assigning a dedicated GPU to each stage of a sequential pipeline results in high costs and low hardware utilization due to idle time.
- Standard serving tools often lack the coordination to share GPU memory effectively, leading to manual configuration errors or system crashes.
- The Superlinked Inference Engine (SIE) supports over 100 models through a unified API with three core primitives: extract, score, and generate.
- SIE manages GPU memory dynamically by loading models on demand and using Least Recently Used (LRU) eviction when memory is constrained.
- To minimize compute waste, SIE batches requests with similar estimated compute costs to avoid excessive padding of shorter inputs.
- SIE includes a model catalog that pre-packages configurations for various architectures, simplifying the deployment of diverse model families.