The Awide Data Processor (ADP) moves the KV cache out of GPU memory, so LLM inference serves the same users on half the servers. Less electricity consumed, less hardware manufactured, fewer CO2 emissions per token generated.
Energy efficiency is not a side effect of our technology. It is the point: acceleration and sustainability are the same engineering problem, solved once.
Based on our reference LLM inference deployment: two GPU servers drawing 20-24 kW consolidated into one with ADP at 10-12 kW for the same workload. See the measurements
Data centers already account for roughly 1.5% of global electricity use, and the IEA projects that figure to more than double by 2030 as AI adoption accelerates. Every watt saved inside the data center is saved around the clock, every day of the year — and it saves a second time in the cooling system that no longer has to remove it.
The industry cannot decarbonize by adding clean supply alone. Demand has to grow slower than workloads do — and that is exactly what hardware acceleration delivers.
more concurrent users on the same GPUs, under the same latency SLA
fewer servers and rack units for the same workload — hardware that never has to be built
lower power draw and cooling load for the same work, around the clock
The ADP moves data-intensive work off general-purpose processors onto dedicated silicon that does the same job with far less power. The effect shows up in four places at once.
ADP consolidates the fleet: in our reference deployment two GPU servers collapse into one serving the same workload. That halves the electricity drawn every hour of every day, and removes the embodied carbon of the hardware that never has to be built.
KV-cache offloading keeps power-hungry GPUs generating tokens instead of recomputing context they have already seen. Higher utilization means fewer GPUs — and fewer megawatts — for the same AI inference throughput.
The same inference workload drops from 14U to 7U and from 20-24 kW to 10-12 kW. That relieves the two limits data centers actually run into first, floor space and power per rack, without building anything new.
Every watt not consumed by a CPU is a watt the cooling plant never has to remove. Efficiency at the silicon level compounds through power delivery and cooling — the savings are larger than the chip alone.
The Net-Zero Industry Act is the European Union's framework for scaling up the technologies Europe needs to decarbonize. It targets EU net-zero manufacturing capacity covering at least 40% of the Union's annual deployment needs by 2030, and energy-efficiency technologies are among the net-zero technologies it covers.
Efficient data infrastructure is part of that path. As AI and data workloads grow, hardware acceleration keeps their energy footprint in check instead of letting it scale with demand — reducing the electricity, and the emissions behind it, that each unit of computing requires.
Read the EU Net-Zero Industry Act