Tag
This paper presents SLAI T-Rex, a full-parameter post-training optimization framework for trillion-parameter MoE models on Ascend NPU SuperPOD, achieving 34.22% MFU and outperforming GPT-5.4-Mini on Operations Research tasks by 3.98 percentage points.
openPangu-2.0-Flash is a 92B-parameter MoE model with 6B activated parameters, trained on Ascend, featuring 512k context length and fast thinking capabilities. It achieves strong performance on reasoning and coding benchmarks, using architectural innovations like MLA attention and multi-token prediction.
openPangu-2.0-Flash is a 92B MoE model with 6B activated parameters, 512k context, trained on Ascend with 34T tokens, incorporating slow/fast thinking and multiple RL training stages.
A user asks about the feasibility of running GLM-5.2 at 4-bit quantization on four Ascend GX10s or DGX Sparks, wondering about speed and memory for 100k context.
Huawei announced openPangu 2.0, an open-source large model with 505B total parameters and a 28:1 sparsity ratio, optimized for Ascend computing and HarmonyOS, with key components to be open-sourced starting June 30.
Huawei has open-sourced its CANN software toolkit to compete with Nvidia's CUDA, and DeepSeek V4 shows significant inference performance improvements on Huawei Ascend chips.