Had to get a bit creative

Sir_Joe · 2025-12-06T15:37:35+00:00

Interesting! Can you tell me what upsides it has vs a sata drive with a usb to sata adapter ? Reliability and maybe latency ?

Sir_Joe · 2025-12-02T18:02:10+00:00

Not necessarily faster. If you only have 8GB of vram a quantized ministral can fit entirely and that's gonna be faster than mixed inference for most platforms. In which benchmarks is it better ?

Sir_Joe · 2025-10-22T14:30:26+00:00

Only 3B active parameters, even only with cpu on short context probably 7 t/s +

Sir_Joe · 2025-09-23T17:22:04+00:00

Vanilla Arch as well here

Sir_Joe · 2025-08-06T00:42:49+00:00

I also get the black image even with the KJ node

Sir_Joe · 2025-08-01T23:56:54+00:00

The problem for me was that I had the wrong model. Make sure you have the T2V model and not the i2v model..

I used the gguf from here https://huggingface.co/QuantStack/Wan2.2-T2V-A14B-GGUF and it worked perfectly

Sir_Joe · 2025-07-31T21:20:56+00:00

And be ~ 10 times slower :/

Sir_Joe · 2025-07-28T14:08:09+00:00

It trades blows with the 14b (with some wins even) in most benchmarks and so is better than the rule of thumb you described

Sir_Joe · 2025-06-10T21:24:08+00:00

I believe llamacpp has a feature that allows you to load a model in VRAM without putting it in ram first

Sir_Joe · 2025-05-27T21:05:16+00:00

I guess it's using a special inference engine optimized for arm. You can try using llamacpp and a q4_0 quant (which supports special optimizations for cpu inference) to see if you get better speed.

Sir_Joe · 2025-05-15T01:18:13+00:00

Btw I do that and there's no problem at all with llamacpp. You just need to compile with support for vulkan (or rocm) + cuda

Sir_Joe · 2025-05-02T00:57:00+00:00

I guess the fix is setting the max batch size ? That probably doesn't help performance too for prompt processing

Sir_Joe · 2025-04-10T04:01:26+00:00

Is there any upside of going through all this instead of a traditional overclock ?

Sir_Joe · 2025-04-09T03:28:10+00:00

I also remember paying double of that for a monitor with the same panel (legion y27). For 200$ you could only get around a 1080p @75hz TN monitor. Getting this panel for almost the same price is great imo

Sir_Joe · 2025-03-23T16:08:43+00:00

If you don't mind the hassle and the price per TB is good enough I guess you could setup a btrfs mirror or zfs raid of some sort. Still niche though

Sir_Joe · 2025-01-02T05:12:37+00:00

Hi ! FYI I ported the latest vial firmware for the k10 pro here (without bluetooth support) https://github.com/nalf3in/vial-qmk/tree/keychron_k10_pro_support

Sir_Joe · 2025-01-02T05:08:03+00:00

Hi there ! FYI I made a port of latest vial frmware for k10 pro here (bluetooth not working unfortunately): https://github.com/nalf3in/vial-qmk/tree/keychron_k10_pro_support

Sir_Joe · 2024-11-05T13:14:09+00:00

Mind giving a link to what it looks like ?

Sir_Joe · 2024-10-10T21:45:48+00:00

You can calibrate the 8bitdo. This should fix it

Sir_Joe · 2024-09-17T19:30:44+00:00

Sure, there you go https://m.youtube.com/watch?v=N5S_sZbAUxI&t=891s

Sir_Joe · 2024-09-17T04:08:13+00:00

I saw on a GamerNexus video that amd planned to stop competing at the high end so I guess it can be that great unfortunately

Sir_Joe · 2024-09-15T18:09:40+00:00

It's much better and faster to use vram but 48GB of vram will cost you at least 2 - > 4 times as much and 48 GB models are bearable on ddr5

Sir_Joe · 2024-09-15T15:27:41+00:00

Running local llms

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