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Huawei Cloud Developer Alliance
Huawei Cloud Developer Alliance
Apr 29, 2026 · Artificial Intelligence

Deploy DeepSeek‑V4 on Ascend NPU with Kthena in 3 Minutes (Prefill‑Decode Separation)

This guide walks through deploying the DeepSeek‑V4‑Flash model on Ascend NPU using Kthena’s ModelRoute, detailing the Prefill‑Decode (P/D) separation architecture, KV cache transfer via Mooncake, configuration of ModelServing and ModelRoute resources, and flexible scaling of Prefill and Decode replicas for optimal performance.

Ascend NPUDeepSeek V4KV cache
0 likes · 22 min read
Deploy DeepSeek‑V4 on Ascend NPU with Kthena in 3 Minutes (Prefill‑Decode Separation)
Huawei Cloud Developer Alliance
Huawei Cloud Developer Alliance
Apr 13, 2026 · Artificial Intelligence

How AReaL v1.0 Enables Scalable Agentic RL on Ascend NPU with AWEX Weight Sync

The new AReaL v1.0 release brings full Ascend NPU support, detailed installation guides, and a best‑practice example for training a 30B MoE model across four nodes, while the integrated AWEX weight‑sync mechanism dramatically reduces synchronization time, improving efficiency and stability for large‑scale Agentic RL workloads.

AWEXAscend NPUDistributed Training
0 likes · 12 min read
How AReaL v1.0 Enables Scalable Agentic RL on Ascend NPU with AWEX Weight Sync
AIWalker
AIWalker
Apr 13, 2025 · Artificial Intelligence

Huawei Pangu Ultra: 135B Ascend‑Native Dense LLM Without Nvidia GPUs

Huawei's Pangu Ultra introduces a 135‑billion‑parameter dense language model trained entirely on Ascend NPUs, detailing novel stability architectures, a domain‑aware tokenizer, multi‑stage pre‑training, extensive system optimizations, and benchmark results that surpass leading models such as Llama 405B and DeepSeek‑R1.

Ascend NPUDense ModelSystem Optimization
0 likes · 15 min read
Huawei Pangu Ultra: 135B Ascend‑Native Dense LLM Without Nvidia GPUs