China Telecom's AI subsidiary has open-sourced the next generation of its TeleChat lineage: on September 17, Xing4.0-29B-A4B weights landed simultaneously on Hugging Face, ModelScope and Modelers, joined by FP8 and GGUF variants, all under an Apache-2.0 license and published under the XingChen-AGI organization.

A 29B-total, 4B-active sparse MoE

Architecturally this is a highly sparse mixture-of-experts model: 29B total parameters with only 4B activated per token; 64 routed experts with 4 active per token plus 1 shared expert; MLA attention, 40 layers, hidden size 3584. Context is natively 256K, extensible to 512K. The vendor positions it as agent-oriented, describing the architecture as mHC + MLA + MTP, and MTP appears as the speculative-decoding method in the official vLLM launch command.

The real story: trained entirely on Ascend

The heaviest claim in the model card is that Xing4.0-29B-A4B is the first model of this scale trained entirely on the Ascend NPU platform with the MindSpore framework (vendor's own wording, a single-publisher claim). Training ran on Ascend 910C clusters; through fine-grained MoE communication optimization, selective recomputation, DVM automatic graph-operator fusion and Ascend C fused operators, overall training throughput improved by roughly 96% over out-of-the-box settings. The README explicitly thanks the DeepSeek team for architecture design inspiration.

Benchmarks: agent workloads are home turf

In vendor-submitted evaluations, Xing4.0-29B-A4B scores 75.0 on SWE-bench Verified, versus 53.0 for Gemma4-26B-A4B and 76.0 for Qwen3.6-35B-A3B; 57.5 on Terminal-Bench 2.1, clearly ahead of 30.0 and 51.5; SWE-bench Multilingual at 66.0, Claw-Eval at 76.55 and DeepresearchBII at 60.80. It trades blows on math and tool use: AIME2026 is 90.0 against Qwen3.6's 92.7, and Tau3-Bench 64.63 against 67.2. All figures are vendor-reported, with evaluation configs (temperature, context window, run counts) spelled out in the model card footnotes.

Ecosystem: framework PRs still in flight

Deployment covers vLLM, SGLang and KTransformers; fine-tuning supports LLaMA-Factory and MindFormers; format alignment is done for agent frameworks including OpenCode, Claude Code, OpenClaw and Hermes. Note, however, that support PRs for SGLang, vLLM, TensorRT-LLM, llama.cpp and KTransformers were all still pending review at release time, so short-term deployment means riding PR branches. Community traction is early: two days after listing, HF shows 7,278 downloads over the last month, the GitHub repo sits at 65 stars, and 6 finetunes plus 7 quantizations are already posted.

For a carrier-backed team, this moves the in-house LLM from press release to reproducible artifact; for the industry, a MoE at this scale trained on the Ascend stack matters more than the benchmark table. Model card and weights: Hugging Face.