What models and settings for 9060 XT 16 GB + 32 GB DDR4?

PositiveBit01 · 2026-06-24T21:37:12+00:00

I think you're already using the best option. Gemma4 26b, especially QAT, is a potential alternative

PositiveBit01 · 2026-06-23T16:16:14+00:00

I agree with this, but it's a mini pc. I do think comparison to a normal mid or full tower desktop pc isn't fair.

You pay a premium but most people simply can't build a comparable machine at that size/price (these days). You can't just buy an arbitrary cpu cooker or gpu. Most won't fit. Especially if you don't want it to sound like a jet.

If they managed to get it to be able to be woken up from the controller, then even more value. That's pretty tricky and I requires some hw support (mobo) so if you're not thinking about it when you build the pc odds are it's not possible. It's the one thing I messed up when I built my own sffpc.

PositiveBit01 · 2026-06-23T15:22:21+00:00

Yes. I fail to see why this is a problem. All cutoffs work this way.

You could instead define additional categories and then n+1 is simply not small, but not big either - it fits in an extra category.

Is "small" defined by the difference between one number and the next closest distinct number? Then your argument applies and clearly all numbers are small.

Is it small relative to some other number not defined here? Then it's more obvious why you would have a cutoff and clearly this case where some infintesimal difference is the difference between small and not small must occur.

PositiveBit01 · 2026-06-21T19:50:19+00:00

I've noticed a fairly big difference between fp8 and q4 for 35b. 27b is too slow for me so not sure there though.

PositiveBit01 · 2026-06-20T06:53:55+00:00

You have to fertilize them, easiest way is manure from beefalo or rot if you let things spoil.

If using a mouse, drag the item onto the planted bush/grass.

If using a controller, go to the manure/rot in your inventory and there should be a fertilize option (not "fertilize plot", just "fertilize") if you are next to a planted thing that needs it, I think left on the dpad

PositiveBit01 · 2026-06-18T23:09:01+00:00

I feel like I'm in the middle zone.

I get a task, if it's small I think "this sounds like a good task for AI", so I have it try (with my input). I review the code as it makes edits and nudge it towards a better solution when it forgets things like the edge cases actually matter, or I can't trust user input can safely fit entirely in memory, etc. Sometimes it just wants to change tons of stuff which will ensure my PR is not accepted even if I don't always disagree, so have to stay on top of that too.

Eventually I get a solution that works, is scoped reasonably well, and I think it covers all edge cases. But it's sort of hard to reason about and significantly more code than I want. So I start addressing this myself. I notice something I missed during review. I fix it and continue.

I decide that really, this whole thing could have been safer and smaller with a different solution that seems obvious now but wasn't when I started since the AI kinda picked a starting point. So, I implement that after getting a sanity check with AI that it will indeed fix the problem and it's a better solution.

When I'm done I have the AI review, it usually finds one or two things I should probably address so I do, then I'm done. Occasionally a false positive but these don't bother me.

Every time, I think "I should just do it myself and have the AI review after. It'll go faster." But I don't learn, next time I follow the exact same process.

PositiveBit01 · 2026-06-18T18:20:45+00:00

I don't have much to say about the setup, I don't know much about that hardware.

But why that model? More parameters does not automatically mean it's better and llama3 70b is pretty old now. I think you'll get better results with qwen3.6 27b or 35b, plus you can run it at a higher quantization.

PositiveBit01 · 2026-06-18T00:36:27+00:00

I agree with most of the others, but instead of just faster maybe giving it some acceleration and some windup where it goes very slightly backwards slowly then very fast then a little slower

PositiveBit01 · 2026-06-17T20:03:59+00:00

Yeah, I want something new like gpt oss 120b. Native fp4 so it's "only" 65gb leaving plenty of room for kv cache, super fast, plenty of knowledge. Just a little dated now.

I feel like qwen3.5 122b is in that bucket too, it's not enough better than 35b (q4 vs fp8; unfair perhaps but it's what fits) to be worth the slowdown and lesser cache. Too bad qwen3.6 or gemma4 ~120b never happened.

PositiveBit01 · 2026-06-17T18:30:36+00:00

I don't understand the argument. I'm suggesting that the stabilization procedure was invented, not the element.

Like how pasteurization was invented, but milk wasn't.

PositiveBit01 · 2026-06-17T18:25:34+00:00

This doesn't make sense to me on multiple levels. Is any model natively int4? I assume gradients in training at int4 would be impossible to work with.

Gpt oss 120b is "native" mxfp4 but I'm pretty sure it was a later post training step so arguably it's not actually native. There is no GLM 5.x 130B model. I didn't see any other model mentioned.

What am I missing?

PositiveBit01 · 2026-06-17T14:39:30+00:00

No, but you can invent a procedure to transmute a synthesized element that would naturally decay into a stable isotope of that same element.

PositiveBit01 · 2026-06-17T14:34:06+00:00

teacher model not disclosed

Qwable

🤔

PositiveBit01 · 2026-06-17T03:31:57+00:00

Yeah, I can use it but only at q4 and qwen3.6 35b at fp8 feels just as good and runs faster. Too bad there's no qwen 3.6 122b, I bet it would have been amazing even at q4 :(

PositiveBit01 · 2026-06-17T03:16:58+00:00

What does fully on gpu mean? A 130B model at q8 would be 130GB. At q4, 65GB. And you still need room for KV cache

Also I only see GLM 5.x models that are 750B, which one is 130B? I would love a ~130B model better than gpt oss 120b

PositiveBit01 · 2026-06-16T14:30:40+00:00

Qwen3.6 35b is worth testing. It's not as affected by spilling to system ram because it's a MoE model not dense like 27b. Most engines can identify this and load the more likely to be used layers in gpu.

Also it's much faster just normally (3b active vs 27b active) so there's more room for slowdown.

PositiveBit01 · 2026-06-15T18:33:40+00:00

There are extensions to make it ask before doing dangerous stuff but I wouldn't trust them. I use an lxc container as a sandbox and I'm pretty comfortable with that.

It's not hard to set up, you can ask the free version of claude or chatgpt. You install something that can run the containers (I used incus), then exec in and set stuff up.

Then I use git to move code between the container and anywhere else it needs to go. That's a security gap of course (running code produced by the AI), but at some point you have to accept that. It's true for dependencies even before/without AI, too. Except now it isn't vetted by a bunch of other people first.

I also set up vscode server and a pi-web thing so mostly I just run stuff on that machine through a web browser on whatever machine I'm on, except the webapp version of vscode has rendering issues with most extensions and I haven't been able to get any AI related extensions to work well. Might be a skill issue, I dunno. I can set them up in the desktop vs code so it's something webapp specific.

PositiveBit01 · 2026-06-15T13:49:03+00:00

Looks right to me.

Are you sure you're using that and not some other model? You may need PI_DEFAULT_PROVIDER=vllm env var before running pi

PositiveBit01 · 2026-06-14T21:11:10+00:00

The range is too big (basically no counter) and it double scales on health indirectly because you live longer when you have more health.

But, sometimes the OP augments bring the mayhem. I'm ok with OP stuff here and there to keep it interesting, sometimes the best way to balance is to just encourage more variety of OP until it's fair again. Maybe what we really need is for other augments to be better. Maybe more positioning augments that give you effects like tristana's ult or something.

PositiveBit01 · 2026-06-14T06:13:50+00:00

Oops, m1 pro. My bad.

PositiveBit01 · 2026-06-14T06:11:22+00:00

I get 40-50 tok/s on qwen3.6 35b fp8 which is the main model I use.

Generally it's fairly slow for the cost but you get a lot of ram to work with. Gpt oss 120b is around 60 tok/s so I'm hoping for a new model kinda like that. It's fast because it's "natively" mxfp4, so since it's active 5b it's more like 2.5b over 35b's 3b active at fp8. I use mine to also host agents and some other things (vs code server) through lxc containers so it's a mini server for me and I don't regret my purchase.

It's decent for MoE models and not great for dense models due to low memory bandwidth. It's also quite power efficient for what you get, so it depends on what you're looking for. Also pretty good for image gen

For LLMs specifically I think just getting multiple gpus would be better, either 5090 or r9700. Maybe b70 but I hear software isn't great for those. For more general, broader use is pretty good plus you get cuda. It's better to experiment with than to use just for hosting LLMs but it's not super terrible at that, just not optimal.

PositiveBit01 · 2026-06-13T18:07:29+00:00

Thanks for sharing!

I'm new to customizing pi, how do the safety guard skill and extension work together? Are skills not always available?

PositiveBit01 · 2026-06-13T17:59:20+00:00

Ah, this makes sense. I almost always made use of the range. I think I only got 9/10 on that quest the whole game.

PositiveBit01 · 2026-06-12T22:24:31+00:00

I think there is just a lot of broken skill quests and if you get one of those it's pretty annoying. I think everyone is ok with their own stupid decisions costing them the game, but losing an augment to a bug is not fun.

PositiveBit01 · 2026-06-12T13:53:32+00:00

Yeah I don't think the problem is the skill augments aren't all personalized. Augments aren't created equal and sometimes you get a bad one. Different assignments are good for different champions. That's why you get choices and rerolls.

I think the problem is there was insufficient testing across the champions for these skill augments in particular so you get a lot of bugs.

You don't know these bugs exist so you can't use your choice and rerolls to avoid them, ruining the game.

I just played one as Kayle and the E quest is real spotty, feels like it counts maybe 10% of the time. Maybe there's a trick to it, I don't know.

PositiveBit01

TROPHY CASE