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Life sciences · Preprint
arXiv · September 3, 2026
Early or partial results. Treat as a signal, not a conclusion.
This preprint describes hardware-aware algorithmic optimizations (Direct-P and a causal backward path) for accelerating attention computation using FP4 tensor cores on NVIDIA Blackwell GPUs. Reported speedups range from 1.14× to 2.13× over bfloat16 baseline on forward inference and single-GPU training, but the work is unreviewed, lacks comparison to alternative methods, and does not validate practical training stability or convergence on end tasks.
Systems optimization and performance benchmarking study. Intervention: Direct-P algorithm and FP4/FP8 quantization-aware backward pass for attention.. Compared with: bfloat16 baseline attention computation.
Direct-P achieves up to 2.13× the bfloat16 forward throughput on NVIDIA GB200 for noncausal inference Causal backward path accelerates a complete single-GPU 8-billion-parameter update by up to 1.14× Distributed training using MXFP4 probabilities and values diverges in all tested trajectories
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Early-stage algorithmic and systems optimization work demonstrating proof-of-concept speedups on a specific hardware platform, without validation against established baselines or clinical/practical outcomes.
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Blackwell's 4-bit floating-point (FP4) tensor cores do not automatically make attention faster because softmax conversion and on-chip dependencies dominate once its matrix products shrink. We address this with \emph{Direct-P} for noncausal inference and a causal path that passes the forward quantization directly into backward. Direct-P maps scores directly to FP4 probabilities and reaches up to 2.13$\times$ the bfloat16 (BF16) forward throughput on an NVIDIA GB200. The causal path reconstructs probabilities from saved quantized queries and keys and uses 8-bit floating-point (FP8) gradient operands, accelerating a complete single-GPU 8-billion-parameter update by up to 1.14$\times$. Matched distributed training retains FP8 probabilities and values; every tested MXFP4 probability/value training trajectory diverges.
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