[R] Deterministic attention-transformer with measured energy savings on H100 (0.63 J/token)

Reddit r/LocalLLaMA 07/13/26, 10:31 PM Papers

attention-transformer rust cuda energy-efficiency deterministic open-source benchmark

Summary

The article presents a custom Rust + CUDA attention-transformer engine achieving 0.63 J/token energy efficiency on H100 with bit-exact determinism, surpassing typical models, and plans open-source release after patent.

I’ve been working on a custom Rust + CUDA attention-transformer engine (GAE + ATE + WNSM + reversible training) aimed at determinism and real energy efficiency. Latest sustained numbers on H100 NVL (28-layer 7B-class stack, continuous batch): Throughput: ~403 tokens/second Energy: 0.63 J/token Power: ~254 W median board power The engine is bit-exact (AUDIT verified), supports both inference and training on the same stack, and runs on multiple hardware backends (H100, M2 Pro, consumer GPUs, WebGPU). Full benchmarks, methods, and evidence are on the site: https://luxiedge.com Open-source planned after patent issuance this month (dual license). Curious if anyone has thoughts on the energy numbers or similar work.

Original Article

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