Originally built during internal performance work, SHIP takes a system-level approach to inference — optimising memory handling, cache behaviour, scheduling, and token generation as a unified system rather than isolated components.

In early real-world tests, SHIP achieved up to a 91% reduction in GPU usage and speed improvements of up to 12×, alongside gains in memory efficiency and cost per token.

Rethinking the Inference Stack

Most AI inference optimisation focuses on individual layers such as model compression or cache tuning. SHIP instead reworks the entire inference lifecycle, introducing a multi-layered architecture that compounds efficiency gains across memory, compute, and throughput — key constraints in large-scale AI deployment.

Built Outside the AI Establishment

SHIP was developed by a team known for web infrastructure rather than AI research.

"This came out of solving real constraints in our own systems," said founder Adam Kerr.

"We weren't trying to reinvent AI — just make it faster and more efficient. The results exceeded expectations, including reducing cost per million tokens from $49 to $4."

Why It Matters

As AI scales, infrastructure — not models — is becoming the primary bottleneck. Improvements in memory utilisation, throughput, and cost per inference directly impact operating costs, with even small gains delivering significant savings at scale.

What's Next

Efficiency is emerging as a defining challenge in AI as GPU demand continues to outpace supply. SHIP reflects a broader trend of impactful innovation coming from smaller, systems-focused teams.

About Sitecove

Sitecove is an Australian web infrastructure company focused on hosting and performance optimisation for small to medium businesses. Founded in 2022 by Adam Kerr.

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