> That does not scale anywhere near as well as Transformers in compute spend. It's paper/research novelty. Nobody will be doing this for production. What exact

> That does not scale anywhere near as well as Transformers in compute spend. It's paper/research novelty. Nobody will be doing this for production. What exactly makes you so confident? The world is not just labs that can afford billion dollar datacentres and selling access to SOTA LLMs at $30/Mtokens. Transformers are highly unsuitable for many applications for a variety of reasons and non-linear RNNs trained via parallel methods are an extremely attractive value proposition and will likely feature in production in the next products I work on. > I guess there's some misunderstanding here because Qwen is 100% a transformer, not a hybrid RNN/LSTM whatever. See the Qwen3.5 Huggingface description: https://huggingface.co/Qwen/Qwen3.5-27B https://huggingface.co/Qwen/Qwen3.5-27B > Efficient Hybrid Architecture: Gated Delta Networks combined with sparse Mixture-of-Experts deliver high-throughput inference with minimal latency and cost overhead.