**Xing4.0-29B-A4B: China Telecom's Moe Model Trained on Ascend NPUs**

> Xing4.0-29B-A4B is China Telecom's new 29B MoE model trained on Ascend NPUs, benchmarked against Gemma4-26B-A4B and Qwen3.6-35B-A3B. Blog / Xing4.0-29B-A4B: China Telecom's Agent-Focused MoE Model, Explained Xing4.0-29B-A4B TeleChat3 Ascend NPU LLM Xing4.0-29B-A4B: China Telecom's Agent-Focused MoE Model, Explained Xing4.0-29B-A4B is China Telecom's new 29B MoE model trained on Ascend NPUs, benchmarked against Gemma4-26B-A4B and Qwen3.6-35B-A3B. Edited by Luis Chavez-Mattos , Director of Product · September 18, 2026 · RSS What is Xing4.0-29B-A4B? Xing4.0-29B-A4B is an open-weight mixture-of-experts (MoE) language model released by China Telecom Artificial Intelligence Technology Co., Ltd. It’s the latest entry in the Xing series, formerly known as TeleChat, and it’s built specifically for agentic work: tool calling, multi-step planning, coding, and terminal-based tasks. The model has 29 billion total parameters but only activates 4 billion per token, and it’s notable for being trained entirely on Huawei’s Ascend NPU platform rather than Nvidia GPUs. TL;DR Xing4.0-29B-A4B is a 29B-parameter MoE model with only 4B active parameters per token, released by China Telecom under an Apache 2.0 license. The model natively supports a 256K context window , extensible to 512K, and uses an mHC + MLA + MTP architecture designed for long-context agent tasks. It’s described as the first model of this scale trained entirely on Ascend NPU hardware using the MindSpore/MindFormers stack, rather than the usual Nvidia GPU pipeline. China Telecom reports a roughly 96% training throughput improvement over out-of-the-box performance through MoE communication tuning, selective recomputation, and custom fused operators. On the model card’s own benchmarks, Xing4.0-29B-A4B leads on Terminal-Bench 2.1, Claw-Eval, and DeepresearchBII against Gemma4-26B-A4B and Qwen3.6-35B-A3B, but trails Qwen3.6 slightly on SWE-bench Verified and SWE-bench Multilingual. The model supports deployment through vLLM, SGLang, and KTransformers , and integrates with agent frameworks including OpenCode, Claude Code, OpenClaw, and Hermes. Fine-tuning is supported via LLaMA-Factory and MindFormers , positioning the model for domain-specific customization on tasks like contract auditing and table understanding. Remy doesn't write the code. It manages the agents who do. AGENTS ASSIGNED TO THIS BUILD R Remy Product Manager Agent Leading Design Engineer QA Deploy Remy runs the project. The specialists do the work. You work with the PM, not the implementers. The world's most powerful product manager agent Try Remy today What’s under the hood architecturally? Xing4.0-29B-A4B runs on a combination the model card calls mHC + MLA + MTP. MLA (multi-head latent attention) is the same attention mechanism family popularized by DeepSeek’s V2/V3 models, designed to compress the key-value cache and cut memory overhead during inference, which matters a lot when you’re pushing context windows out to 256K or 512K tokens. MTP refers to multi-token prediction, a training technique that lets the model predict several tokens ahead per step, generally improving both training efficiency and inference speed. mHC is the piece China Telecom highlights as requiring custom engineering work, including dedicated fused operators built for Ascend hardware. Structurally, the model has 40 layers, a hidden size of 3584, and a MoE layout with 64 routed experts plus 1 shared expert, of which 4 experts activate per token. That 4B-active-parameter footprint is what keeps inference costs down relative to a dense 29B model, while still giving the router access to a large pool of specialized experts. Why does training on Ascend NPUs matter? Most large language models r...

**来源信息**
- **来源**：Hacker News：AI 热帖
- **分类**：ai-models
- **发布时间**：2026-09-21 00:48（北京时间）
- **原文**：[打开原文](https://www.mindstudio.ai/blog/xing4-0-29b-a4b-model-release)