{
  "video_id": "reddit_1utkqfg",
  "channel_slug": "LocalLLaMA",
  "channel_handle": "r/LocalLLaMA",
  "title": "Why are MoE models so belittled?",
  "url": "https://www.reddit.com/r/LocalLLaMA/comments/1utkqfg/why_are_moe_models_so_belittled/",
  "external_url": null,
  "upload_date": "20260711",
  "published_at": "2026-07-11T13:52:48+00:00",
  "transcript": "E.g *\"Qwen 3.5 122B is just 10B active, so it's no where close to the dense 27B model\"*\n\nThat is the main sentiment around here and it puzzles me. If a 122B is just worth 10B, then why does model providers bother creating an MoE model when they could've just released a dense 10B model? Heck the 10B dense would run faster than the 122B MoE (no routing overhead), which negates the supposed (*only advantage of MoE is speed*) argument. It sure is not that simple.\n\nI mean yes it's only 10B active at a time, but it comes down to the router's effectiveness at choosing what 10B experts to activate. So, the more effective the router is, the closer the model to realize its total parameter potential. So perhaps it's a little more nuances, ie some MoE architectures are better than other MoE architectures. Right? I may be missing something.\n\n\n\n--- Top Comments ---\n\n\n[121 upvotes] In my experience with Qwen series, even though Qwen 3.6 27B beats Qwen 3.6 35B-A3B, it does not fully beat older Qwen 3.5 122B-A10B, especially at longer context. This is why many hoped at the time there will be 3.6 122B release. When I need a small quick model, I still keep going back to Qwen 3.5 122B-A10B - it is faster than 27B, while handling long context tasks (like condensing long context, large document summaries, etc.) at comparable quality or better.\n\n[55 upvotes] It is indeed more nuanced. This sub will be biased towards whatever they can run at their own hardware. If everyone had 8x B200 locally already, they'd say that Qwen 27B sucks and Kimi 2.x is miles better and runs  just as fast and if everyone had a 32GB RAM laptop they'd compliment Qwen 3.6 35b a3b packing a huge punch while 27B would be \"outdated the moment it came out\" \n\nOn the API side, Qwen 3.6 27B is served at quite expensive rates given its capabilites, so there's certainly a downside to 27b dense model - it's not fit for economical mass-scale deployment.\n\n[45 upvotes] 35b-a3b vs 27b dense, yeah the 27b clearly wins.\n\nQwen3.5's much larger 122b moe is overall much more capable than the Qwen3.5 27b in my usage, but others may still find 27b better in some use cases. \n\n...also be careful that you're not comparing the newer Qwen3.6 27b to Qwen 3.5 122b.  \n\nQwen 3.6 is a newer model with lots of improvements, which is probably why lots of people prefer it. \n\n[34 upvotes] I think this is confusing for people with limited VRAM because they assume that a 122B MoE model has the same requirements as a 122B dense model, since “VRAM is the main limitation”. However, the actual inference cost is determined mainly by the number of active parameters. Assuming you can run both a 122B model and a 27B model, a 122B A10B model will be faster and therefore cheaper to run.\n\nThat’s why GLM Air, quantized to 4 bits, is still a great model. But people on Reddit are not aware of this because “GLM 5.2 is better, and everyone knows that GLM 5.2 is a local model, so who cares about GLM Air?”\n\n[27 upvotes] Qwen3.6 27b is about the only dense model that gets any love, it gets compared to 35b and older 3.5 Qwens. It's the perfect size for high end consumer hardware and runs just fast enough for inference.\n\nAside from that everything big is MoE these days, DeepSeek, GLM, Minimax. It's clearly the solution for enterprise hardware. Silly statement.",
  "transcript_chars": 3314,
  "ingested_at": "2026-07-12T01:30:04.016966+00:00",
  "source": "reddit",
  "yt_meta": {
    "score": 134,
    "upvote_ratio": 0.85,
    "num_comments": 131,
    "author": "ParaboloidalCrest",
    "is_self": true
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}