pkroll

People on reddit have definitely pointed out that sageattention will speed up the renders.

And it's literally the first day. Someone will make a distilled 4-8 step LoRA and we're off to the races.

Edit: did a couple of 10 second long 864/480 i2v videos on my RTX Pro 6000: sageattention bumps them up 33%, that is to say, 140.89 seconds without sageattention becomes 105.69 with sageattention on (if using the KJ Sageattention node, "allow_compile" doesn't seem to affect it, just "sage_attention" set to "auto" works fine).

EasyCache also appears to work, but does affect quality, at least with the default threshold or even down to 0.10. Still, at 0.10 threshold the same render above, with sageattention, is down to 71.33 seconds, so depending on your use case the quality hit might be worth it. Also it seems that with EasyCache the video still matches the un-EasyCached video (with the same seed), so you could use it to do seed hunting.

Since no one else posted it... I have open-webui pointed at a linux box with 128 gig of ram and an RTX Pro 6000, and after a couple of runs on trivia, had it do one of Open WebUI's conversation starters: "Show me a code snippet of a website's sticky header in CSS and JavaScript."

72.06 t/s. That's the full Qwen 3.6 27B model BF16, using MTP, running on Ollama. Yes I know I should bite the bullet and get vllm running on that box.

That was, also, at a 570 watt limit: I normally run a little less, but when I first tried this I actually forgot I had set the limit to 300 (it's a hot day, I figured why fight the A/C?), and at 300 watts the same question came back at 69.38 t/s. (The extra power matters more for compute bound things, the difference in generating LTX2.3 videos is considerably higher... but still not linear.)

Check the LLM benchmarks once it's out: it's such a common use case for these kinds of machines, you won't be waiting long.

Really? Why? I'd bet the opposite: the worst of the things happening with OpenClaw aren't being revealed.

It's likely that I'm seeing this from my deep into ComfyUI bubble. My impression was that AUTOMATIC1111 and Forge and the like, were fading as ComfyUI was the "what people ended up on" no matter which AI generation framework they started with. But I don't know that there are any real stats on usage of these programs, so it's entirely possible that AUTOMATIC1111/Forge/InvokeAI are being used by more people than ComfyUI.

Did you test some local image gen software in that you installed the Python code on the github page for a local model, which is clearly a LOT for a normal user... or did you look at ComfyUI, which is how most people are running local video and image models? There are "just install this" versions, which eases the path for users (but it's still, admittedly, chaos beneath the surface).

Mass shootings in the US, are a little over 1 a day. School shootings are a subset, and as cudgy says, so far 13 this year.

"NTSTC content at 23.976 I'd hope the player would just speed up at that point, but even if not... judder at 120 Hz is better than at 60 Hz."

I'd bet money when TVs are advertised at 120 FPS, they're really 119.88 FPS, so no judder showing 23.976 FPS and the other NTSC-off display rates.

It's a regular expression, and OS/2 was referred to in many ways, so try something like: "os-2|os\/2|os2" (without the quotes)

I get 136 results, from that query.

As jasonjmcghee says, they're available... but if you go to ollama.com and set models to "newest" you'll see Mistral (specifically mistral-small3.2 at this writing) because they seem to not sort the models based on newest update: only newest "group" or however you'd phrase it. So you need to scroll down to "qwen3" to see it's been updated.

Slightly frustrating. But good to know.

I use Markdown Viewer, in Chrome: I'd bet there are multiple equivalents in Firefox and Safari. Well. I don't know what Safari's extension universe is like but it seems likely.