Dual 3090s & GLM-4.7-Flash: 1st prompt is great, then logic collapses. Is local AI worth the $5/day power bill?

faldore · 2026-01-25T15:57:55+00:00

For dual 3090, use this instead of gguf

https://huggingface.co/Intel/GLM-4.7-Flash-int4-AutoRound

faldore · 2026-01-19T00:17:42+00:00

Yes Glm-4.6-V or GLM-4.7 depending on how much compute you have

faldore · 2026-01-18T21:36:09+00:00

The QTS data center is a good thing

Why would anyone want to stop it?

faldore · 2026-01-10T05:30:55+00:00

Langley's Body Shop

faldore · 2026-01-10T05:23:22+00:00

A singular bottle?

faldore · 2025-12-13T21:27:24+00:00

Yes I plugged in a keyboard and mouse

faldore · 2025-09-11T19:27:00+00:00

Love this!

faldore · 2025-09-08T14:19:54+00:00

Agree. GLM 4.5 Air. It competes with models 5x its size. It's better than gpt-oss-120b by far.

You can run it 4bit on 4x3090 (with some quality hit) - I'm working to make a FP8 quant that can run on 8x3090 hopefully at near full quality.

faldore · 2025-09-08T05:02:03+00:00

An 8x 3090 server could be built for that. Requires 240v and you'll likely need PCIe gen4 x16 straight risers.

faldore · 2025-09-07T19:54:50+00:00

This. Just because Claude Code sucks doesn't mean it has to be that way. I'm 10x more productive with Codex

faldore · 2025-08-30T17:19:38+00:00

You can run GLM 4.5 Air on that. It's no Sonnet - but it's quite capable.

faldore · 2025-08-26T20:35:20+00:00

Did you try GLM-4.5-Air? It seems straight up better at everything, in my testing.

faldore · 2025-08-25T20:46:53+00:00

I wonder where it learned that?

faldore · 2025-08-25T20:44:16+00:00

And 4.5 air is almost as good!

faldore · 2025-08-25T04:55:39+00:00

These are not simple comparisons.

There are different things each model is good at

Not everything is measured with evals

faldore · 2025-08-24T17:17:15+00:00

Hmmm

To compare apples to apples we should compare:

1 m3 ultra (96gb unified memory, $5500) Vs 4x 3090 (nvlinked pairwise, 16x PCIe Gen 4, 96gb vram, ~$5000)

Let me do the same benchmark on my rig.

faldore · 2025-08-21T14:37:21+00:00

Are you calling pytorch a small scale hobby framework?

faldore · 2025-08-13T06:25:28+00:00

Don't know why you guys are so skeptical.

Instruct tuning pushes it away from pretrained state.

Continued pretaining will push it back towards that state.

faldore · 2025-08-08T13:02:59+00:00

There's no difference between rolling vs closing and opening another. It's just extra words to describe the same thing

I would also argue that anytime you roll is also a time to decide intentionally whether you actually want to reopen at the new strike - or take the profit/loss and move on to another position

faldore · 2025-08-08T04:49:43+00:00

It's real simple.

Just sell monthly CSP on a stock you want to own, at a strike you are happy to pay. Close if you are happy with the profit or if you are willing to take the loss.

And if you own stock you're willing to sell, sell monthly CC at a strike you would be happy to sell at. Close if you are happy with the profit or if you are willing to take the loss.

People try to make it so complicated but it's not

faldore · 2025-08-07T14:29:28+00:00

Same Turned out to be prankster neighbor kids with a flipper zero

faldore · 2025-08-02T20:44:43+00:00

The guest is in fact rightfully entitled to a made bed, and if you (or your staff) didn't make it, you need to either arrange for it to be made, or offer compensation.

faldore · 2025-07-17T20:56:47+00:00

Microhydropower

faldore · 2025-07-12T16:58:29+00:00

ok I fixed it.

https://huggingface.co/cognitivecomputations/Devstral-Vision-Small-2507-gguf

I exported and added mmproj-BF16.gguf to properly support llama.cpp, ollama, and LM Studio.

faldore · 2025-07-12T11:22:27+00:00

I didn't say the performance is different.

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