u/xdcfret1

A few days ago I tried generating a single html file for a solar system using different LLMs. Since Qwen3.8 27B is released now, I thought I should give the same test to this model too.

My system:

• AMD AI Pro R9700 (32 GB VRAM)
• 32 GB system RAM

Model: unsloth/Qwen3.8-27B-Q6_K (The best quant I can run locally)

Context Size: 98,048 tokens

It's truly a thinking model, it really really likes to think. It was thinking for over 14+ minutes (at 35-40 t/s in my system with MTP)

Total time taken for the html file: 17m 9s

Total generated tokens: 42,530 tokens (reasoning + result)

Initial prompt:

>Write a self contained html file for a living solar system. It should be beautiful.

And it did not work. It was a blank canvas with some controls that did not work. There was a error in the code. I gave the error back to the model. It suggested a fix, after applying the fix I got the orbits to be visible. But there were still no sun or planets.

The console still had some more errors which I provided back to the model. It identified some more issues and provided fix for them. I applied the fix and it was working now.

Honestly, the final result was the best interface generated by any model in my tests so far.

HTML File: https://limewire.com/d/BmGv5#5nNSQz6Zhj

We have come a long way from that video. So I thought why not try to recreate it again?

Just spent some time testing the new LTX 2.5 workflows in ComfyUI, and I wanted to share my initial impressions.

The Good:

Speed: It is honestly super fast. I'm getting generations much quicker than previous versions. Diffusion Fidelity Rendering is the real deal. Image Quality: A significant step up. The details are sharper, and it feels less "muddy". Audio: I know audio in video models can be hit-or-miss, but in my runs, the audio alignment and quality were actually good.

The "Needs Work":

Physics: It's still hallucinating a bit here. Objects sometimes clip or move in unnatural ways, so don't expect perfect realism just yet.

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Overall, it feels like a solid update. What do you think?

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The attached video was super impressive for the first 6 seconds. Then she started riding backwards.

I wanted to see if I could run Laguna S 2.1 on my personal PC.

My system:

• AMD AI Pro R9700 (32 GB VRAM)
• 32 GB system RAM

Definitely not the ideal setup for a 118B model, but I managed to get it running.

I used Unsloth's UD_IQ4_XS quant with llama.cpp. With Q8_0 KV quantization and 25 layers of experts offloaded to the CPU, I was able to get:

• ~66k context
• ~20 tokens/s

I mainly wanted to compare it against Qwen3.6 27B MTP Q6_K, which is currently the best model I can comfortably run. That one gives me 200k+ context and around 50 tokens/s.

For the comparison, I gave both models the same simple prompt:

>Write a self-contained HTML file for a living solar system. No external libraries or modules. It should be beautiful.

Honestly, I wasn't that impressed with the Laguna output. I know the recommended quant is Q4_K_M and I'm using UD_IQ4_XS instead, so that definitely isn't ideal. But I was still expecting to see a more noticeable improvement over a good 27B model.

Another thing I noticed is that Laguna didn't seem to think/reason at all for this prompt. Not sure if that's expected with this setup or if I have something configured wrong.

I'm attaching the HTML files from both models if anyone wants to compare.

One question for people following Laguna: if a ternary Bonsai version of Laguna S 2.1 comes out, do you think it would perform significantly better on hardware like this, or is this about what I should expect?

HTML Files: https://limewire.com/d/URqgx#srL2p87cG7

I wanted to see if I could run Laguna S 2.1 on my personal PC.

My system:

• AMD AI Pro R9700 (32 GB VRAM)
• 32 GB system RAM

Definitely not the ideal setup for a 118B model, but I managed to get it running.

I used Unsloth's UD_IQ4_XS quant with llama.cpp. With Q8_0 KV quantization and 25 layers of experts offloaded to the CPU, I was able to get:

• ~66k context
• ~20 tokens/s

I mainly wanted to compare it against Qwen3.6 27B MTP Q6_K, which is currently the best model I can comfortably run. That one gives me 200k+ context and around 50 tokens/s.

For the comparison, I gave both models the same simple prompt:

>Write a self-contained HTML file for a living solar system. No external libraries or modules. It should be beautiful.

Honestly, I wasn't that impressed with the Laguna output. I know the recommended quant is Q4_K_M and I'm using UD_IQ4_XS instead, so that definitely isn't ideal. But I was still expecting to see a more noticeable improvement over a good 27B model.

Another thing I noticed is that Laguna didn't seem to think/reason at all for this prompt. Not sure if that's expected with this setup or if I have something configured wrong.

I'm attaching the HTML files from both models if anyone wants to compare.

One question for people following Laguna: if a ternary Bonsai version of Laguna S 2.1 comes out, do you think it would perform significantly better on hardware like this, or is this about what I should expect?

HTML Files: https://limewire.com/d/URqgx#srL2p87cG7

System info:

OS: Ubuntu

CPU: Ryzen 9 7900X

GPU: RX 9070 XT

RAM: 32 GB

My Krea2 workflow with base fp8 + turbo lora was working fine yesterday. Today it can't generate images anymore.

My other workflow with Flux2 Klein 9B image edit is working fine, so I don't think it is a GPU issue.

I tried taking help from Chat GPT and Calude but could not find a solution even after hours of debugging.

Did anyone else face this issue? Any help or suggestions would be appreciated.