MTT S5000 Fully Adapts Qwen3.5 Models

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#gpu-adaptation #open-models #domestic-hardwaremtt-s5000

💡Run Alibaba's latest open LLMs on Chinese GPU without Nvidia dependency (mid-scale models now supported)

⚡ 30-Second TL;DR

What Changed

MTT S5000 supports Qwen3.5-35B-A3B, Qwen3.5-122B-A10B, Qwen3.5-27B

Why It Matters

This boosts domestic AI hardware options in China, reducing reliance on foreign GPUs for running Alibaba's open models. AI practitioners gain cost-effective alternatives for deploying mid-scale LLMs at scale.

What To Do Next

Download Qwen3.5-122B-A10B and test inference benchmarks on MTT S5000 hardware.

Who should care:Developers & AI Engineers

🧠 Deep Insight

Web-grounded analysis with 5 cited sources.

🔑 Enhanced Key Takeaways

•Moore Threads demonstrated DeepSeek V3 performance on the MTT S5000, achieving 1000 tokens per second in Decode and 4000 tokens per second in Prefill, slightly ahead of Nvidia's Hopper lineup.[1]
•MTT S5000 was used in benchmarks by 51WORLD's next-gen simulation platform, showing approximately 1.47× performance gains in FP32, FP16, and INT8 precision modes.[5]
•Preceding the S5000, Moore Threads' MTT S4000 GPU with 128 Tensor Cores and 48GB memory already supported Qwen models for training and inference via its MUSA architecture.[3]

🔮 Future ImplicationsAI analysis grounded in cited sources

Moore Threads MTT S5000 will expand adoption of domestic Chinese AI GPUs in model training ecosystems.

Full adaptation of Alibaba's Qwen3.5 models enables comprehensive training and inference support on MTT S5000, building on prior Qwen compatibility in S4000 and recent DeepSeek V3 benchmarks outperforming Hopper.

This adaptation strengthens Moore Threads' position against Nvidia in China's AI compute market.

MTT S5000's demonstrated superior DeepSeek V3 inference speeds compared to Hopper, combined with Qwen3.5 support, highlights competitive performance for large model workloads.

⏳ Timeline

2026-02

MTT S4000 supports Qwen for LLM training and inference with 128 Tensor Cores.

2026-02

MTT S5000 demonstrates DeepSeek V3 inference at 1000 tps Decode and 4000 tps Prefill.

2026-02

51WORLD simulation platform benchmarks MTT S5000 with 1.47× gains in key precisions.

2026-02

Moore Threads announces full adaptation of Qwen3.5 models on MTT S5000.

📎 Sources (5)

Factual claims are grounded in the sources below. Forward-looking analysis is AI-generated interpretation.

🔥Read original article on 36氪

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