70to195GW
Projected AI data center power demand, 2026 to 2030.
Training and inference combined, rounded, in McKinsey’s October 2026 continued momentum scenario.
Source: McKinseyAI inference systems
More intelligence.
A lighter footprint.
Low power. Low cost. Low latency.
We’re developing AI inference systems with hardware and software designed together.
Born of light. Built by brilliant minds.
The challenge
AI is becoming essential.
Its resource demands
are becoming harder to ignore.
Inference is what happens when a trained model answers a question, writes code or powers an application. As those uses grow, the cost of running models matters as much as what they can do.
~60%
Of AI demand expected to come from inference by 2030.
From the same McKinsey outlook. Running models overtakes training as the larger share of AI demand.
Source: McKinseyMemory
Large models and growing context compete for expensive memory. Capacity and bandwidth can limit useful output.
Power
Electricity, grid connections and cooling increasingly shape where new AI capacity can be built.
Capital
The bill extends beyond compute: memory, networking, buildings and cooling all affect deployment economics.
Latency
People and applications need timely responses at real serving loads, alongside high total throughput.
What we’re building
Hardware and software.
Designed for inference.
Designed together.
Andromedai is developing a new compute architecture and the systems around it. Our work spans how a model processes a prompt, generates a response and serves many requests.
We start with the whole deployment: the model, the workload, the power budget and the cost of delivering useful output.
Energy efficiency
Less energy
per useful result.
Targeting more useful AI output from the power available, with energy use considered across the complete system.
Deployment economics
A lower cost
to serve.
Designing around the cost of the whole deployment, including hardware, supporting infrastructure and electricity.
Responsiveness
Fast answers.
Useful capacity.
Designed for very low latency, from the first response to each generated token.
Built for inference at scale.
We’re interested in conversations with cloud inference providers and GPU cloud operators, enterprises running private AI services, model developers serving at scale, AI agent and coding assistant platforms, and national AI infrastructure providers.
Discuss your workloadOur purpose
Designed with
Earth’s resources in mind.
AI can help people discover, create and solve problems. The infrastructure behind it should respect the energy, water and land that communities depend on.
More efficient computing, for the people of Earth.
Our engineering
Many disciplines.
One working system.
Building inference systems brings together expertise across hardware, software and the practical realities of making a product work.
Select a star to explore how the disciplines connect.
Explore the constellation
Hardware
Silicon, electronics and compute hardware, considered as parts of one complete system.
Connected disciplines
Get in touch
Let’s build
what comes next.
For partnerships, investment, introductions or an interest in joining the team, start with a conversation.
contact@andromedai.ai
We’d like to hear from you.