Huawei Ascend Adds Support for Kimi K3.1 Pro on Atlas A3 and Ascend 950 SuperPoDs

Beijing-based AI company HongYun has unveiled HyperLM V2.5, a new version of its platform for combined training and inference of large models. This was reported by IT Home and ifeng Tech on November 3rd. The company claims to be the first to achieve deep adaptation to the Huawei Ascend 950DT chip, full compatibility with the air-cooled Atlas 850E SuperPoD system, and successful technical certification from Huawei for Ascend Native.

HongYun believes that defining support is often too vague, where a model is only considered supported after installation and token generation. The company aims to assist government and enterprise clients using private deployments in achieving maximum performance from new accelerators.

The company describes the Ascend 950DT as a flagship chip for high-throughput tasks such as extended chat interactions, processing long documents, and continuous agent reasoning. Furthermore, it is noted that the air-cooled Atlas 850E system can be integrated into existing data centers without requiring infrastructure upgrades.

HyperLM V2.5 adapts at multiple levels. At the interconnect level, the platform supports Huawei's UnifiedBus 2.0 technology, which provides a system interconnect bandwidth of 13.4 TB/s, preventing bottlenecks during traffic transfer between cards—a crucial factor for long contexts or splitting prefill and decoding stages. At the operator level, the system is optimized for mxFP4 inference, enabling 14.2 PFLOPS per node.

The platform supports the latest open-source models, including Qwen, GLM, and DeepSeek, and its scheduler has been rebuilt considering the memory profile and throughput of the 950DT.

The release of HyperLM V2.5 includes five new features. Prefill and decoding splitting allows the two stages to be isolated into independent resource pools so they do not compete during peak loads. Multi-level KV caching reuses system prompts, frequently encountered contexts, and fixed session templates to reduce redundant computations. vNPU virtualization segments the Ascend 950DT, 910C, and 910B chips, providing granular quotas and isolation for AI Core computation and HBM memory, and also supports hot migration and automatic rescheduling upon hardware failure.

A unified gateway model, compatible with the OpenAI protocol, has also been added, which manages routing, rate limiting, beta releases, and A/B testing. Comprehensive monitoring across the entire stack tracks load, memory usage, throughput, latency, and QPS, providing alerts and automated fault localization.

For regulated industries such as government administration, energy, transportation, and finance, the platform provides role separation for administrator, security officer, and auditor, multi-level classification control, complete operation logs, and tracking of computational resources, model call audits, and usage statistics per project.

HongYun was incubated by the Western Institute of Computing Technology of the Chinese Academy of Sciences, and its core team consists of employees from the Institute of Computing Technology and Sugon. In 2020, the company joined Huawei's Ascend partnership program. The company plans to continue development based on Ascend and Hygon DCU hardware, focusing particularly on heterogeneous scheduling, inference acceleration, caching, and agent applications.

Lenovo has released the first AI host system in its Kaitian lineup, intended for China's IT innovation market (Xinchuang). This 1.6-liter desktop computer integrates a Zhaoxin processor with a Houmo AI NPU, enabling the local execution of models around 35 billion parameters.

The device is equipped with an eight-core Zhaoxin KX-7000 processor and the Houmo Liqing LQ50 NPU. In the standard configuration, the system provides 160 TOPS of computing power in INT8 format, which can reach 320 TOPS with the NPU option. The NPU features 24 GB of dedicated memory with a bandwidth of 153.6 GB/s. The host runs on Kylin OS and comes pre-installed with Lenovo's Tianxi AI Pro agent.

The company tested the device using open models, including the Qwen3.5 and Qwen3.6-35B-A3B series. When utilizing the 24 GB NPU and W4A8 quantization, the Qwen3.6-35B-A3B model generated text at a rate of approximately 28.99 tokens per second during local inference. The system supports input data up to 200,000 tokens for processing long documents and multi-turn tasks. The Houmo platform provides developers with tools for profiling, compiling, runtime management, and model quantization.

Lenovo positions this host as a perpetually active digital employee designed for round-the-clock operation. This allows agents to perform scheduled tasks such as local knowledge retrieval, document drafting, and information summarization, with output processing occurring directly on the device rather than through cloud tokens, thus keeping data local. During the event, Tianxi AI Pro testers instructed the device to check a mobile operator's application, and the device successfully executed a test call, measured the delay of a verification SMS code, and provided a report.

Regarding thermal dissipation, the processor and NPU have separate air channels behind the hexagonal front panel with high-pressure fans. Lenovo reports that at idle, the chassis temperature is about 33 degrees Celsius in a 25-degree room, and under full load, it reaches 41 degrees. In a 35-degree room, the temperature reaches approximately 46 degrees under full load. The noise level is 19.4 dB in standby mode and about 29.5 dB during full NPU inference. The device weighs about 1.5 kg and is equipped with two independent 2.5GbE ports.

It should be noted that the Kaitian host differs from the Lenovo AI Host P7, which was introduced in October with Houmo M50 and CIX P1 chips.

The first computing cluster built on SuperPoD with 1024 Ascend 950DT cards from Huawei was activated in Wuhan. The launch took place on October 28 at the Huawei Artificial Intelligence conference for Hubei Province, which was part of the Huawei 2026 China roadshow.

The investment for this cluster was provided by the Hubei Provincial Science and Technology Investment Group, which is the provincial state investor. According to this group, the project implementation took only one month from the start of work to commissioning, which is a relatively short period for a system of this scale.

The deployment is a single liquid-cooled SuperPoD. One node is equipped with 1024 Ascend NPU accelerator cards. Both compute and interconnect cabinets are fully equipped with liquid cooling systems. The system is capable of providing 1 EFLOPS performance in FP8 format and has 256 TB of globally unified memory. Inter-card bandwidth reaches terabytes per second, and end-to-end latency is in the microseconds.

The design architecture is based on UnifiedBus, Huawei's internal connection architecture for SuperPoD. This technology allows each card in the cluster to directly access other cards, enabling the 1024 NPUs to function more like one large accelerator rather than many individual servers connected via a standard network. Huawei positions this setup for training trillion-parameter models and for running AI agent inference.

The main advantage of the SuperPoD approach is direct access. When training large models, a significant amount of time is spent moving data between chips. Maintaining unified memory across the pod and within its interconnect architecture reduces the volume of traffic that must pass through slower channels.

The cluster is designed to meet local needs. It aims to provide model developers and companies in Hubei with an accessible source of computing power for training large models, as well as for creating, configuring, and operating industry AI agents. Local sources note that this fills a gap in high-performance computing supply in the province.

The launch in Wuhan also marks the early practical implementation of the Ascend 950 generation. Earlier this year, Huawei demonstrated a physical SuperPoD based on Ascend 950 and reported that over 750 units of the previous generation Ascend 384 SuperPoD had already been commercially deployed. The 950DT cluster in Wuhan demonstrates the transition of the new SuperPoD design from demonstration equipment to regional AI computing centers.

Huawei did not disclose data on the cluster's power consumption or detailed utilization metrics; the numerical values mentioned above were stated at the launch event.