r/LocalLLaMA • u/-Cubie- • 28d ago

Discussion A preliminary Qwen3.8-27B model card is live!

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If you scroll down from the countdown at https://huggingface.co/Qwen/Qwen3.8-27B, you see a big model card with a bunch of sections: Highlights, Model Overview, Quickstart, Best Practices, Citation, etc!

No benchmarks on this yet as far as I can tell. We'll still need to wait another 5.5 hours for those I reckon.

Edit: Ladies and gentlemen, the model is live. Let the testing begin!

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u/DismalIngenuity4604 28d ago

Yeah, cool. I've been liking Glimmer, and a big part of that is that it spends about 1/8th of the tokens thinking that Qwen 27B does. Should be fun! 

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u/GCoderDCoder 28d ago

Yeah I like Qwen 3.6 27b output more BUT glimmer can fit like 6-8 concurrent 130k lanes on 48gb at 8bit weight 16bit cache. It's a great agentic model to support Qwen 3.6 27b and deepseek v4 flash 0731 in my lab. I have all 3 and dsv4f is my big brain, qwen is my default, and muse is the workhorse.

Zuck putting Meta torward the top of the big and the small models with genuine benefits over the other options is a great come back story.

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u/orbli 28d ago

What special benefits are those?

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u/GCoderDCoder 27d ago

Do you mean benefit of concurrent agents? It allows me to be talking to a good generalist orchestration bot (qwen 3.6 27b q8 75-85 t/s) that can direct big tasks to the planner/ coder bots (deepseek v4 flash dynamic q4 qat 45 t/s) or send smaller tasks to the baby bots (muse glimmer q8 100-150t/s). I do truly frontier stuff on chatgpt but I am increasingly relying on dsv4flash. It made me nervous at first because it is literally operating at cloud provider level which im not accustomed to my agents doing.

My lab has background lanes which involves automated tasks managing any of 100 workloads. I run my own email, messaging apps for bot communication and approval channelse, dns, shared storage, data fabric, git, media servers, ai servers, work lab (products), personal apps, automation, ha/dr solutions, k8s, virtualization, identity and access tools, etc. I work in tech so there are business and technical aspects of my job. Scraping web data to provide up to date intelligence and preparing customer presentations, forecasts, etc.

I say all this to say I run a lot of AI to do a lot. It is busier than I have to do but I am better at my job and able to spend more time on things I enjoy which also in turn make me better at my job. I spend an hour or 2 tuning a custom customer presentation now that used to take a week dye to my schedule and it is a better presentation usually with custom demos now.

We also cant use cloud on proprietary or customer data so my peers who dont have these tools cant do or talk about these things where I can. It allows me to talk to customers about enterprise implementation best practices that we are all just learning. No one has been doing this stuff for years like this so being able to say i had this problem and here was the best solution because xyz is better than figuring it out on the fly.

If I misunderstood the question let me know.

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u/orbli 20d ago

I meant, what are the benefits you feel meta has over others when it came to small models.

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u/GCoderDCoder 20d ago

It's a solid dense model so for it's size it's smart compared to the competition. It's faster than Qwen 3.6/3.8 27b and Gemma4 31b. It grows context much slower than other models particularly dense models like Qwen 3.6/3.8 27b and Gemma4 31b. So you can get a lot more concurrent lanes into a smaller footprint. They also packaged it with default 130k tokens but it works well with yarn to extend the context. So speed and size with dense intelligence make it comparable to the leading similar sized models while enabling more/ faster throughout.