A production LLM inference architecture

Reduce AI Inference Costs by up to 91%

A production-validated architecture that increases throughput, reduces memory usage, and dramatically improves cost per token in large scale environments.

Built, tested, and deployed. Game changing.
Built, tested, and deployed.
Game changing.
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Inference Q&A's

What are the results?
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In the most demanding scenario possible for SC (70B model, 8k context, 32-user concurrency), SHIP lowers cost per 1 million output tokens from US$49.02 to US$4.24. That is a reduction of US$44.78 per 1 million output tokens. At 100 million output tokens per month, the same scenario implies a monthly serving-cost delta of roughly US$4,478.

Based on real-world?
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Yes. SHIP has been evaluated/tested/pounded under production-representative workloads, including sustained concurrency, real token streams, and continuous inference conditions.

Can it be deployed on existing infra?
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Yes. SHIP is designed to integrate with modern GPU-based inference environments, including common serving stacks. It can be deployed on existing infrastructure and scaled across single-node or multi-node environments, depending on your requirements.

Is SHIP available publicly or open source?
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No. SHIP is a proprietary architecture developed by SiteCove and is not publicly available.

What are the plans for SHIP?
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While Sitecove specialises in web hosting and performance solutions for small to medium businesses, the scale and enterprise demand associated with deploying SHIP globally require a different level of infrastructure and distribution. Rather than underutilising its potential, we are making SHIP available for sale to an organisation better positioned to deploy it at scale. This approach ensures the technology reaches its full impact, while allowing Sitecove to focus on its core offerings.