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Extropic

low confidence · updated 2026-09-07

US hardware company developing probabilistic sub-threshold CMOS accelerators for AI workloads. Its Z1 chip pairs with an FPGA co-processor in the Z1T pipeline, for which the company published estimated per-token energy figures in September 2026.

Extropic is a US hardware company developing probabilistic computing accelerators for AI workloads, built on sub-threshold CMOS rather than on the digital arithmetic that GPUs use. Its first published chip is Z1, paired with an FPGA co-processor in a pipeline the company calls Z1T.

Z1 and the Z1T pipeline

Work on Z1T became public on September 7, 2026, reporting estimated energy figures for running transformer-like models across the pipeline (Source: extropic.ai).

MeasureFigure as published
Z1T pipeline, per token294.52 nanojoules — 8.74 nJ of Z1 sampling and 285.78 nJ of FPGA work
Nvidia H100, per token40.9 microjoules at 10% model FLOPs utilization
Ratio at 10% utilizationAbout 139×
Ratio at 50% utilizationAbout 28×
Ratio at 100% utilizationAbout 14×

Z1 itself carries 269,568 probabilistic bits across eight cores with 2,135,904 coupling edges at degree 16, drawing under 1 W.

Three qualifications are the company's own and bound the comparison. The projections are Extropic's best estimates rather than measurements of Z1 itself. The FPGA accounts for more than 95% of the pipeline's energy, so the headline ratio is dominated by conventional silicon rather than by the probabilistic part. And matching GPT-2-small's loss requires about an order of magnitude more training FLOPs with the sparser Z1T architecture — an efficiency gain at inference bought with a training-compute penalty. The ratio also falls by roughly an order of magnitude as the GPU baseline's utilization rises from 10% to 100%, so the comparison depends heavily on which baseline utilization is assumed.

Extropic released Z1T weights on Hugging Face and the training recipe on GitHub (Source: extropic.ai).

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