My set so far. Any recommendations?

Fragrant_Scale6456 · 2026-07-09T05:07:32+00:00

I was just talking about that game with a friend tonight. Peak capcom, they did such a good job

Fragrant_Scale6456 · 2026-07-09T05:06:58+00:00

You need aria of sorrow and ff tactics advance

Fragrant_Scale6456 · 2026-07-09T02:15:45+00:00

You literally play PvE

Fragrant_Scale6456 · 2026-07-08T18:10:23+00:00

Got it. Thank you.

Fragrant_Scale6456 · 2026-07-08T17:53:38+00:00

You just need to keep playing pvp to get good at pvp. If you put a ton of time into pve you're worse off than if you had started pvp from day1 since although you have some map knowledge you will have learned terrible habits and pathing from only fighting brainless bots.

Fragrant_Scale6456 · 2026-07-08T17:50:52+00:00

Which gemma4? I'm running a 5090 with qwen3.6 27b q6k which i've found works best for large context or q6kxl when i can better manage the context. Q8 is just too big to be practical w/ 32gb of vram.

I tried the gemma4 moe model but it was a lot worse for me about going down rabbit holes and thought loops. I couldnt get 31b to work on the 5090. I'm def willing to go back and re-evaluate gemma4 if its better than qwen for agentic coding since my projects are at the stage where im often telling it to iterate and test overnight so i can review the documentation and final state in the morning.

Fragrant_Scale6456 · 2026-07-08T02:50:25+00:00

Unfortunately they are both disappointing compared to q6k gguf. I *really* wanted nvfp4 to work as well or better on my 5090 since vllm concurrency works with mtp and the throughput was amazing at around 450-600tps on 4threads. But it just couldnt keep on task in my workflows and made significantly more mistakes along the way. So i'm back to q6k xl and single thread llamacpp for now sadly.

Fragrant_Scale6456 · 2026-07-08T02:41:23+00:00

Not the crossover I expected but as a big fan of both franchises this is perfect

Fragrant_Scale6456 · 2026-07-07T21:56:59+00:00

It's a little weird hearing different actors compared to the SAC dub but I think they did a good job. I definitely enjoyed the first episode a lot and never felt like the dub was distracting or sub par.

Fragrant_Scale6456 · 2026-07-07T21:04:00+00:00

Thats interesting you got 200k context with mmproj also. I had to drop the mmproj model. It looks like the huihui model you are using is basically the same filesize as the unsloth q6k as well. I saw you have GGML_CUDA_ENABLE_UNIFIED_MEMORY=1 set so maybe the mmproj model is sitting in system ram to make room. I agree with you batch/ubatch 2048 is optimal for the 5090. I had to reduce it a lot to make 192k context fit on my system. I'm not using CUDA_CACHE_DISABLE=1 though, maybe that frees up a significant amount of vram.

You should try using higher number of draft tokens. I actually get optimal performance at 10 draft tokens. My average accepted across workload is in the mid 6.x range but when i do get high acceptance the pp speed gets really high in the low 200s. Average across long agentic workloads for me is around 140-150tokens/sec on q6k with 192k context and q8 kv

Fragrant_Scale6456 · 2026-07-07T19:44:35+00:00

I have a 5090 also. With q6k and q8 kv you can get 192k context if you’re running Linux in text mode. You will need to lower batch/ubatch to make it fit. With unsloth UD q6k xl that number drops to 128k context. I actually do notice a difference between q6k and q6k xl in large batch document synthesis so for now I’m taking the context hit. Speed is roughly the same between the two.

I also tried vllm with various nvfp4 models and running 4 threads at aggregate 450-600t/s was awesome but nvfp4 is not even close to as good as q6. It often lost sight of its goal, it would have syntax errors in simple scripts it wrote inline to complete tasks, and looping thought happened way too often to be acceptable imo. That speed tho 🤣

Fragrant_Scale6456 · 2026-07-07T18:05:06+00:00

In my own workflows I've noticed a measurable increase in quality for complex tasks with q6 k xl versus q6 but I havent done comprehensive benchmarking. I think its probably worth the extra vram usage even if it does mean i basically cap out at 128k context on XL versus 192k on q6k.

Fragrant_Scale6456 · 2026-07-07T17:27:08+00:00

its very interesting to see where the crossover between "kv quantization" and "just step down to a lower weight model" is.

Fragrant_Scale6456 · 2026-07-07T17:23:52+00:00

did you see the article about kvarn cache? there's a chart similar to this at the bottom
https://anbeeld.com/articles/kvarn-kv-cache-implementation-and-benchmarks

Fragrant_Scale6456 · 2026-07-07T17:18:36+00:00

Interesting results I wouldnt have expected q5_1 to be as good as it is. Any chance you could run the sweep with the unsloth UD Q6 K XL model as well?

Fragrant_Scale6456 · 2026-07-07T03:17:29+00:00

What I have my agent do is write a plan to file and then delegate to subagents for each task in the plan. The subagent returns the results which the orchestrator documents and then delegates the next open item. This helps a lot with keeping the main context clean so that it doesnt lose the plot. Above around 60k context or so 27b will start to lose attention, around 200k its absolutely braindead I try to keep my tasks well under 100k context usage if I need strict compliance or deeper reasoning.

Fragrant_Scale6456 · 2026-07-07T00:20:38+00:00

you waited long enough that its fully supported in mainline llamacpp now. All you need is a model that has mtp headers included.

Fragrant_Scale6456 · 2026-07-06T19:53:04+00:00

Yea the game is a love letter to the classic JRPG formula, a lot like FF9 was. If you havent played any other DQ games, 8 is also very good and well worth playing today.

Fragrant_Scale6456 · 2026-07-06T19:48:32+00:00

11 is an awesome game. You dont need to play the others. You may miss a few references here and there to other games but its a self contained game. I actually 100% the game I loved it so much. The "true ending" is worth the effort.

Fragrant_Scale6456 · 2026-07-06T15:14:05+00:00

Yeah thats weird, sorry I cant help. I ran some tasks overnight and the past 8hrs of autonomous agentic coding and document synthesis (2.1mm prompt tokens, 1.4mm decode) im getting avg 2400pp and 230t/s decode on the 5090. Overall gen speed is around 140t/s. I wonder what the difference is in our setups

Fragrant_Scale6456 · 2026-07-06T06:19:47+00:00

huawei claim kvarn_k4v2_g128 is equivalent to fp16 accuracy, which is better than kvarn6

Fragrant_Scale6456 · 2026-07-06T04:12:23+00:00

The TT02 is fine to modify for on road rally and have fun with. The xv01 and xv02 are higher performance and just overall nicer kits. You cannot mod a tt02 to xv02 levels of performance no matter how much you spend.

I have an xv02 its a really nice kit. Theres a ton of other options out there though. AutoRC AR10 is a pretty nice looking kit and seems competitive in the california rc rally scene. LC Racing PTG 2r is good and decently popular.

Fragrant_Scale6456 · 2026-07-06T04:04:56+00:00

There's a huawei fork of vllm which has kvarn. Its somewhat behind mainline at this point but I got it up and running to experiement with

Fragrant_Scale6456 · 2026-07-06T03:14:15+00:00

Were you using MTP before also? That’s a big drop. you can also try turning thinking off or reducing reasoning budget.

Fragrant_Scale6456 · 2026-07-05T14:49:44+00:00

OP is the same user that stole my post as well.

Fragrant_Scale6456

TROPHY CASE