Introducing:COHERA – A New Hardware Architecture. More Useful Compute. With Less.
Progress doesn’t have to cost the world so much.
Scaling AI and computing has come from adding more: more compute, more energy, more infrastructure.
But the demand for AI is growing faster than the world can build the power and infrastructure to run it.
When power, memory, and hardware are the limitations, efficiency per joule becomes capacity.
What Does More with Less Look Like?
The fair comparison: the same AI workload. Same answers. Same chip. Different computing architectures. Watch the difference in energy required to get the work done.
Each crate is a stack of finished answers. The glass column is the electricity it took to produce them. Pick a rival, press play, and watch how much each side needs.
COHERA used 735× less electricity per answer than the CPU core, and 3.5× less than the AI-specialist NPU core.
The Urgent Constraint
of energy, storage and large-load projects are stalled in grid connection queues worldwide. New grid infrastructure takes 5–15 years to plan, permit and complete. (IEA, Electricity 2026)
of data-centre demand already comes from AI workloads, and AI is far more than chatbots: checking payments, spotting faults, sorting sensor data. About 70% by 2030. (McKinsey)
This means that there is valuable compute that is stranded.
COHERA unlocks more useful compute with less energy, cooling, memory and infrastructure.
On a standard AI benchmark workload, measured on programmable hardware:
lower energy per answer than a conventional processor core, on the same development board and benchmark.
lower energy per answer than AMD’s AI accelerator, on the same development board and benchmark.
Same job, same answers, same development board. Two separate runs: the processor core on 14 September 2026, AMD’s accelerator on 3 August 2026; each is compared with COHERA’s figure from the same run.
The same work, for less than 1% of the electricity.
New, 23 October 2026: one COHERA engine ran a complete 8-billion-parameter language model, and every token it produced matched the reference exactly. A correctness result, not a speed or energy result.
Measurements as of 4 November 2026.
Internal, board-level measurements on a specific AI inference workload, with outputs checked against a reference. Results are repeatable under the tested conditions and are not representative of all workloads. Programmable silicon means commercially available hardware configured with COHERA’s architecture, enabling real-world measurement and deployment in standard servers without first manufacturing a custom chip.
This is our floor, not our ceiling.
Every COHERA speed and energy measurement on this site was made on a development board: a programmable chip far less efficient than custom silicon, with no custom chip built yet. And it still beats a modern NVIDIA chip on energy per answer. See that comparison →
Custom chips typically need about 12× less dynamic power than programmable ones.
Imagine What Could Be Unlocked.
When computing requires less energy to accomplish the same work, existing resources can support more.
- More AI and compute capacity within the same power envelope
- More productive use of existing infrastructure and greater revenue potential
- Deferred investment in additional power, cooling, and infrastructure
- More real-time intelligence within the same energy budget
- Greater on-device processing with less dependence on the cloud
- Longer battery life, greater autonomy, and more deployment flexibility
More useful compute. From the resources we already have.
More isn’t the only way forward.
The possibility we are exploring is not a trade-off.
Capability and efficiency. Technology and the world it serves.
With a measurement: 735× less electricity per answer than a CPU core, on one image-recognition workload. See exactly which workload →
Go further.
See it for yourself.
We would welcome the chance to walk you through the measurements and explore where COHERA fits for you.
Request a 30-minute walkthrough→ jane@cohera.tech
Jane Dudley, Founder & CEO, CoheraTech, Inc. · Greater Austin, Texas