Qwen: Better, Faster, Cheaper. Sure, All Three
September 17, 2025
No smart software involved. Just a dinobaby’s work.
I spotted another China Smart, U S Dumb write up. Analytics India published “Alibaba Introduces Qwen3-Next as a More Efficient LLM Architecture.” The story caught my attention because it was a high five to the China-linked Alibaba outfit and because it is a signal that India and China are on the path to BFF bliss.
The write up says:
Alibaba’s Qwen team has introduced Qwen3-Next, a new large language model architecture designed to improve efficiency in both training and inference for ultra-long context and large-parameter settings.
The sentence reinforces the better, faster, cheaper sales mantra one beloved by Crazy Eddie.
Here’s another sentence catching my attention:
At its core, Qwen3-Next combines a hybrid attention mechanism with a highly sparse mixture-of-experts (MoE) design, activating just three billion of its 80 billion parameters during inference. The announcement blog explains that the new mechanism allows the base model to match, and in some cases outperform, the dense Qwen3-32B, while using less than 10% of its training compute. In inference, throughput surpasses 10x at context lengths beyond 32,000 tokens.
This passage emphasizes the value of the mixture of experts approach in the faster and cheaper assertions.
Do I believe the data?
Sure, I believe every factoid presented in the better, faster, cheaper marketing of large language models. Personally I find that these models, regardless of development group, are useful for some specific functions. The hallucination issue is the deal breaker. Who wants to kill a person because a smart medical system is making benign out of malignancy? Who wants an autonomous AI underwater drone to take out those college students and not the adversary’s stealth surveillance boat?
Where can you get access this better, faster, cheaper winner? The write up says, “Hugging Face, ModelScope, Alibaba Cloud Model Studio and NVIDIA API Catalog, with support from inference frameworks like SGLang and vLLM.”
Stephen E Arnold, September 17, 2025
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