Even Big Lebowski doesn’t know where the money went: Meta’s "Superintelligence" is a sub-$5 basement-tier Frankenstein
Let’s talk about the Elephant in the room. Mark Zuckerberg is all over the news right now, loudly claiming that Meta is building a "personal superintelligence" for every human and demanding hundreds of thousands of H100s/B200s to "train the future."But when you actually open the repository of your latest "state-of-the-art agentic masterpiece" Muse-Glimmer-30B and look at the configs, it feels like this entire model was slapped together by a drunk intern over a weekend to fit a quarterly KPI. This isn't AI engineering; this is corporate resource laundering.Let’s break down this structural circus piece by piece:1. The Triple Bottleneck / Projector NightmareYour vision encoder is a tiny, generic ViT-G/14 with a hidden size of 1536. Your language backbone expects a hidden_size of 6656. Instead of natively training a multimodal model like any competent lab in 2026 (look at Qwen or Kimi 3), you guys just bought a handful of cheap adapters from AliExpress.First, you compress the 1536 vision tokens down to a bottleneck of "projector_hidden_size": 4096.Then you linearly upscale it to "out_hidden_size": 6144 (probably because you copy-pasted the projector head from an older Llama 3.2 artifact and were too lazy to retrain it).And finally, you forcibly pad or stretch it from 6144 to 6656 right before feeding it into the LLM!Are you serious? You are passing low-dimensional visual data through a sequence of non-linear (GELU) and linear interpolations into a highly non-linear causal probability space. You are injecting flat, upscaled noise into a 6656-dimensional semantic manifold. No wonder this model suffers from cortical blindness and starts hallucinating the moment it looks at a basic UI screenshot or small terminal logs.2. The Tokenizer Config: Copy-Paste CrimeThe tokenizer_config.json is a work of pure comedy.Why are there over 2000 reserved special tokens (<|reserved_special_token_2|> to 2047) just bloating the embedding matrix and wasting VRAM during initialization? Did someone forget to delete their scrap vectors?Why is the dictionary filled with video tokens (<|vid_start|>, <|vid_frame_separator|>) when this specific 30B model does not support video? You literally didn't even bother to clean up the vocabulary from your internal Muse Spark/Llama 4 test runs.And the crown jewel: embedding raw regular expressions inside the tokenizer config to parse XML-like tags (atem:parameter). Forcing an agentic LLM to generate pseudo-HTML and relying on rigid regex strings for tool calling is a structural design flaw. If the model misses a single space, the regex breaks, and the agent freezes. Is this 2018?3. Slided-Attention Leaks: Recycled GimmicksUsing a [Local, Local, Local, Global] hybrid pattern with a 2048 sliding window is not "innovation." It’s a recycled, desperate bandage from the Gemma 2/Mistral era. We all know how these hybrid architectures behave under real pressure. Sure, it looks nice on paper with a "132k context," but on long context chains, the information travels through the network in delayed hops. The model completely loses track of instructions placed in the middle ("Lost in the Middle" phenomenon), and the KV-cache management on local layers creates massive throughput degradation.4. DFlash: A Hardware-Locked Cop-OutInstead of training a proper, lightweight speculative 2B auto-regressive draft model (like you did for Llama 3), you came up with this Block-Diffusion DFlash monster. A 5GB diffusion draft head that requires massive parallel matrix multiplications (GEMM) just to guess packages of tokens. It’s completely useless on consumer hardware like Mac or mid-tier GPUs because it triggers OOM or massive offloading latency. It only achieves your bloated "233 tokens/sec" benchmark on top-tier Nvidia rigs (RTX 5090 / server clusters). This is not democratizing AI; this is optimizing for your own internal data centers.Serious Question: Where is the Budget Going?Meta has an army of thousands of elite Ph.D. researchers and billions of dollars in computational budget. So why does your open-source release look like a stitched-together Frankenstein monster made of incompatible, recycled weights held together by blue duct tape?Stop shouting about "AGI for every primate" from the stage when your actual deployment architecture is a cascade of lazy engineering compromises. We deserve native multimodal architectures, clean vocabularies, and real engineering—not this corporate gaslighting.Bravo!!!
I don't disagree with all of this, but how about taking the narrative out of it and formatting with paragraphs, bullet points and lists so we can actually read what you're saying. On a M5 Pro it performs reasonably (better than Qwen 3.6 or Gemini 4 models of equivalent sizes on some tasks and worse on others, which is exactly what the release stats say.
If you wanted to ignore the structural facts, why even reply? Just to type some empty corporate buzzwords?Let’s talk about your "reasonable performance" on that M5 Pro. You do realize that from an architecture and hardware perspective, the M5 Pro is a massive downgrade compared to even the old M1 Max when it comes to memory bandwidth, right? Your narrow memory bus completely chokes on long contexts, and since ExecuTorch doesn’t have native, hardware-optimized block-diffusion compilation for Apple Metal, your 5GB DFlash draft head is running as a bloated, sub-optimal sequence of matrix operations. You are literally burning your Unified Memory throughput on a hardware-locked cop-out, getting maybe a miserable 1.5x speedup, and calling it "reasonable."And your only defense is "well, it matches the release stats on some tasks"? Seriously? Are benchmarks the only thing that keeps your hype-worshipping tech bubble alive? Anyone with half a brain in AI engineering knows how Meta cooked those stats by forcing Qwen and Gemini to run on their rigid ATEM XML-prompting templates to artificially tank their scores.If you love being fed low-quality engineering on a silver platter just because it has a Meta logo on it, go ahead and make a direct financial donation to Mark Zuckerberg. But unless you have a serious, mathematical argument to counter the triple-upsampling bottleneck from 1536 to 6656 or the memory leaks of their recycled hybrid sliding-attention, don't just post here "to say something."Keep believing the marketing fairy tales while downloading half a terabyte of unoptimized garbage.Read some books-it will help you))
i was about to download the model but checked the reviews first. this guy is the hero gotham deserves but doesn't have because nobody really understands these things, they're now as easy to use that anybody could so marketing hype is the driving factor. i don't care if he writes without spaces, he said the truth. anyone better be prepared to answer in the merit/substance because the AI Police guy is on fire.
i was about to download the model but checked the reviews first. this guy is the hero gotham deserves but doesn't have because nobody really understands these things, they're now as easy to use that anybody could so marketing hype is the driving factor. i don't care if he writes without spaces, he said the truth. anyone better be prepared to answer in the merit/substance because the AI Police guy is on fire.
Sorry to break it to you, but Adikkkk is most likely just an abliterated/Heretic-uncensored version of Qwen 3.6...
I think the best way to evaluate a model is to try it in your own agents.
Most people don’t care about how a model was trained, or even how it works under the hood. They, including me, simply try it and compare it with other models like Qwen and Gemma. It’s like using an iPhone—you may know nothing about how it was built or how it works internally, but you can still judge whether it’s a good product based on your experience using it.
Why are Qwen models so popular?
The simple reason is that the quality is great.
You may be an LLM engineer, so you might notice things that are not as good. I hope this is useful feedback and helps Meta improve their models.
Now, I’m looking forward to the Qwen3.8 27B model, which is going to be released this week, and seeing how it compares with this model.
I launched this model unsloth muse glimmer 30b Q8_0 with draft-dflash 3 using the latest llama.cpp with rocm 7.14.0 dual 9060 XT 16 GB. Everything seems to be working fine: output performance is about 28-35 t/s. Unsloth qwen 3.6 27b Q8_0 on the same setup works 35-40 t/s output (it is a bit more performant). However, it works in a comparable way to Qwen 27b. Maybe MTP would be better, but dflash is fine too, it is better than nothing.
I am also waiting for a new models from Qwen, however a competitor from Meta is fine, just let it be. From my experience I will still use Qwen 3.6 27b, but Muse Glimmer 30b will be an additional validator (like Gemma 4 31b). Do not criticize this model too much.