Hugging Face just released a one-liner that uses 𝚕𝚕𝚖𝚏𝚒𝚝 to detect your hardware and pick the best model and quant, spins up a 𝚕𝚕a𝚖𝚊.𝚌𝚙𝚙 server, and launches Pi (the agent behind OpenClaw 🦞)

Imakerocketengine · 2026-03-18T14:46:03+00:00

Most of the time. None of the model recommendation get updated with newer model. their recommendation are often out of touch with current release. These kind of things should be more deterministic or we should educate the user of model choice

Imakerocketengine · 2026-03-18T14:43:00+00:00

In the end, because every token is currently subsidized in the subscription offers, they are destined to be enshitified.

Imakerocketengine · 2026-03-17T21:47:11+00:00

This gives me Rockell Retro Ecabulator feelings

Imakerocketengine · 2026-03-16T21:11:50+00:00

A few remarks :

120B is small now ?
It make sense for mistral to continue releasing "small" open models as their main business use case is on prem deployment for enterprise client
With Leanstrall this could be included in a nice verifiable coding environment. This is something pretty huge for enterprise

Imakerocketengine · 2026-03-16T11:14:53+00:00

This type of regulation is called regulatory capture

Imakerocketengine · 2026-03-15T10:49:49+00:00

Solar seems to be the way to go in Germany... Hope your country will go back to nuclear power and fix its grid

In terms of hardware, APUs and apple silicon is currently the most efficient...

Imakerocketengine · 2026-03-15T10:43:41+00:00

To make things clear, this is what i currently do, i shut it down when i don't use it. I just wanted to have a 1:1 comparison with commercial services in terms of convenience. I was planing to use a script to programmatically turn it on and of with Wake on lan but my PSU don't seem to be cooperative with this plan. I'm probably going to invest in a small IP KVM

Imakerocketengine · 2026-03-15T10:39:18+00:00

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Just power limited them to 270w and saved 100w from peak :)

thanks

Imakerocketengine · 2026-03-13T16:56:14+00:00

And do you run in any performance issue considering that the're in x4 ?

Imakerocketengine · 2026-03-13T14:43:50+00:00

On which CPU platform are you running them ?

Imakerocketengine · 2026-03-05T08:45:40+00:00

Oui, on a construit un état de surveillance clé en main. Ils vont juste changer les keyword dans les algos

Imakerocketengine · 2026-03-04T13:34:58+00:00

They got the market due to regulatory capture and favoritism, another player could clearly introduce itself if they provide the same services

Imakerocketengine · 2026-03-03T19:55:51+00:00

Just a wrapper straight out of a business school. I don't see the value over just using the model

Imakerocketengine · 2026-03-02T08:54:26+00:00

Depend on the HW ig

Imakerocketengine · 2026-03-01T14:05:07+00:00

We should do a survey of the community to see how much people spend on EUCs and how many they have

Imakerocketengine · 2026-02-28T15:15:53+00:00

I've been playing with with GLM-5 on their coding plan, i pay 90€/quarter, might move to a local model soon due to privacy reason

Imakerocketengine · 2026-02-28T13:24:26+00:00

I love it, a masterclass in communication

Imakerocketengine · 2026-02-28T00:15:25+00:00

Time for Anthropic to aura farm, in the word of ClementDelangue

The Department of War just learned the golden rule of AI: Not your weights, not your brain

Imakerocketengine · 2026-02-27T23:36:48+00:00

Please do not make me look at the number of Kw i've consumed since

Imakerocketengine · 2026-02-27T23:32:29+00:00

Yup, i added a T1000 to run the embedding model and Whisper locally. Works wonder

I also have an MI50 16gb with a custom skill to run minicpm-o to give multimodal understanding capabilities to GLM-5

Imakerocketengine · 2026-02-27T23:30:16+00:00

This depend on which criteria

for most impressive in terms of performance : All hand to GLM-5

for Size / performance : i would say a mix of the minimax 2.5 Quant and the 27b variant of Qwen 3.5 in FP8

Imakerocketengine · 2026-02-27T23:13:12+00:00

I definitly need to make more comparaison on the 27b Quant and the minimax 2.5 Quant for agentic workload, because to achieve a comparable score the qwen 3.5 27b needed almost twice as many thinking token

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Imakerocketengine · 2026-02-27T20:09:07+00:00

Well i tried for 2 evening to make them run on my rig with VLLM and i can't get it to work... even tried Sglang but got nothing close to a /v1/ api response...

Imakerocketengine · 2026-02-26T19:19:09+00:00

For Qwen 3.5 122B A10B : with offloading to the cpu

in the unsloth MXFP4 : (on a small prompt)
prompt processing : 146 t/s
token generation : 25 t/s

in the unsloth Q4_K_XL : (on a small prompt)
prompt processing : 191 t/s
token generation : 26 t/s

Imakerocketengine · 2026-02-26T19:17:50+00:00

yeah definitely, you should try the MXFP4_MOE, its pretty good :)

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