Superlinked (@superlinked) / X

Superlinked

704 posts

Superlinked

@superlinked

Open-source inference for agents.

Joined September 2019

Superlinked
@superlinked
7h
Stop deploying four containers for four models. The industry standard wastes GPU space. SIE handles scoring, encoding, and extracting in one process. Simplify your stack at github.com/superlinked/sie #AI #LLM #Infra
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Superlinked
@superlinked
Jul 27
Stop wasting GPU budget on one-container-per-model setups. The industry default wastes resources. We built SIE to handle multiple models through a single process. Check the repo: github.com/superlinked/sie. #AI #MachineLearning #LLM #GPU
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Superlinked
@superlinked
Jul 27
Can small AI models actually do the job? If you can serve them on a single, older GPU, they might be the better choice for production. Stop chasing the biggest model and start optimizing. Check out the SIE repo for more: github.com/superlinked/sie #AI #LLM #MachineLearning #Tech
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Superlinked
@superlinked
Jul 26
Agentic inference looks just like a modern search pipeline. You need interaction models, sparse matching, and a final re-ranker. But scaling four separate containers is a mess. That is why we built SIE. One process, one API, three primitives. Grab it here: github.c...
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Superlinked
@superlinked
Jul 26
Bundling inference into your platform cuts out network hops and lets you tailor performance to your specific traffic. It makes your system tighter. Ditch the black box approach. Code here: github.com/superlinked/sie #AI #MachineLearning #LLM
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