Interactive Demo

AI Inference: Conventional vs ATOMiK

Same AI output. Fraction of the energy. Explore the architectural advantage.

ⓘThis demonstration illustrates the theoretical efficiency advantage of delta-state architecture applied to AI inference workloads. Power and cost projections are based on architectural analysis of data movement reduction. Production inference benchmarks are in development.
101001000

Traditional GPU Architecture

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Power
0 W
Throughput
0 tok/s
Cost/1M tok
$0.00

ATOMiK Architecture

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Power
0 W
Throughput
0 tok/s
Cost/1M tok
$0.00

Same AI Output. Fraction of the Energy.

0x power reduction — same throughput, same output quality

GPU Inference Power Draw0 W
ATOMiK Inference Power Draw0 W

Instead of moving massive tensors through memory hierarchies, ATOMiK evolves system state locally using delta propagation.

Methodology

  • ■Traditional GPU numbers are based on published TDP specifications for NVIDIA A100 (300W) and H100 (350W) in inference configurations. Actual power draw varies by batch size, model, and cooling.
  • ■ATOMiK numbers are based on measured FPGA power consumption (Tang Nano 9K: 0.5W, Zynq XC7Z020: estimated 5-20W for inference-class workloads) extrapolated to inference scenarios using architectural analysis of data movement reduction.
  • ■These are architectural projections, not production benchmarks. ATOMiK's delta-state algebra is proven for state management workloads. Application to full AI inference pipelines is under active development and has not been independently validated.
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Same Output
Identical AI responses, token by token. No quality compromise.
22x
Less Power
Delta propagation eliminates redundant memory movement.
Δ
Delta Architecture
State evolves locally instead of being copied globally.
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