Each has it's pros and cons. Dense models of equivalent total size obviously do run slower if all else is equal, however, the fact is that 35A3B is absolutely not 'a lot smarter'... in fact, if you set aside the slower inference rates, Qwen3.5 27B is arguably more intelligent and reliable. I use both regularly on a Strix Halo system... the Just see the comparison table here: https://huggingface.co/unsloth/Qwen3.6-35B-A3B-GGUF . The problem that you have to acknowledge if running locally (especially for coding tasks) is that your primary bottleneck quickly becomes prompt processing (NOT token generation) and here the differences between dense and MOE are variable and usually negligible.
replyI was hoping this would be the model to replace our Qwen3.5-27B, but the difference is marginally small. Too risky, I'll pass and wait for the release of a dense version.
replyCould you explain why prompt processing is the bottle neck please? I've seen this behavior but I don't understand why.
replyYou should be able to save a lot on prefill by stashing KV-cache shared prefixes (since KV-cache for plain transformers is an append-only structure) to near-line bulk storage and fetching them in as needed. Not sure why local AI engines don't do this already since it's a natural extension of session save/restore and what's usually called prompt caching.
replyif I understand you correctly, this is essentially what vllm does with their paged cache, if I’ve misunderstood I apologize.
replyPaged Attention is more of a low-level building block, aimed initially at avoiding duplication of shared KV-cache prefixes in large-batch inference. But you're right that it's quite related. The llama.cpp folks are still thinking about it, per a recent discussion from that project: https://github.com/ggml-org/llama.cpp/discussions/21961
reply"…whereas 35A3B is a lot smarter…"
replyMust. Parse. Is this a 35 billion parameter model that needs only 3 billion parameters to be active? (Trying to keep up with this stuff.)
EDIT: A later comment seems to clarify:
"It's a MoE model and the A3B stands for 3 Billion active parameters…"
That makes no sense. If you were just going to release the "more hype-able because it's quicker" model then why have a a poll.
replyWhat? 35B-A3B is not nearly as smart as 27B.
replyOne interesting thing about Qwen3 is that looking at the benchmarks, the 35B-A3B models seem to be only a bit worse than the dense 27B ones. This is very different from Gemma 4, where the 26B-A4B model is much worse on several benchmarks (e.g. Codeforces, HLE) than 31B.
reply> This is very different from Gemma 4, where the 26B-A4B model is much worse on several benchmarks (e.g. Codeforces, HLE) than 31B.
replyWouldn't you totally expect that, since 26A4B is lower on both total and active params? The more sensible comparison would pit Qwen 27B against Gemma 31B and Gemma 26A4B against Qwen 35A3B.
yeah the 27B feels like something completely different. If you use it on long context tasks it performs WAY better than 35b-a3b
replyI've been telling analysts/investors for a long time that dense architectures aren't "worse" than sparse MoEs and to continue to anticipate the see-saw of releases on those two sub-architectures. Glad to continuously be vindicated on this one.
replyFor those who don't believe me. Go take a look at the logprobs of a MoE model and a dense model and let me know if you can notice anything. Researchers sure did.
MoE isn't inherently better, but I do think it's still an under explored space. When your sparse model can do 5 runs on the same prompt in the same time as a dense model takes to generate one, there opens up all sorts of interesting possibilities.
replyYes.
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