← Back to all news
arXiv Machine Learning By Liam Cooper, Shinnung Jeong, Hyeran Jeon, Jeffrey Young, Hyesoon Kim

Accelerating the Mitigation of LLM Inference Nondeterminism Across GPU Architectures

Read the original on arXiv Machine Learning →

The paper addresses the problem of non‑deterministic outputs from large language models (LLMs) when run on different GPU architectures, caused by floating‑point non‑associativity and hardware‑dependent kernel choices. It proposes a set of fixed‑configuration fused‑upcast GEMM kernels that load 16‑bit weights, upcast to FP32, and perform IEEE‑754 compliant reductions in a problem‑shape‑dependent order, ensuring identical linear‑layer outputs across NVIDIA Ampere, Ada, and Hopper GPUs. The new approach achieves 1.17–3.1× faster end‑to‑end performance than existing solutions and halves weight‑memory traffic while maintaining cross‑architecture reproducibility.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv AI
Aug 28

Pushing the Envelope of LLM Inference with Ultra-Low-Bit Quantized Models

The paper reports the development of 2‑bit microkernels for CPUs and mixed‑precision 2‑bit kernels for Intel Xe2 GPUs, achieving near‑roofline performance. Integrated into LLM inference pipelines, these kernels deliver up to 7× speedup over 16‑bit inference on CPUs and 6.7× on GPUs, surpassing the current state‑of‑the‑art bitnet.cpp runtime by 2.2×. The work demonstrates that ultra‑low‑bit LLM models can be deployed efficiently, offering significant gains in latency, memory, throughput, and energy consumption.

By Evangelos Georganas, Dhiraj Kalamkar, Alexander Heinecke, Pradeep Dubey
llmsefficiencybenchmarks
More like this →
arXiv AI
Jun 30

KernelSight-LM: A Kernel-Level LLM Inference Simulator

arXiv:2606. 28565v1 Announce Type: cross Abstract: As large language models (LLMs) move into production serving, practitioners must rapidly evaluate inference performance across diverse hardware, models, and serving parameters to meet cost and latency targets.

By Xiteng Yao, Taeho Kim, Hengzhi Pei, Xinle Liu, Kyle Ulrich, Leonard Lausen, Ashish Khetan, Xiang Song, George Karypis, Martin Herbordt
llmsefficiencybenchmarks
More like this →