BIM Computing

Hardware for AI freedom. Building the local AGI for everyone. Your models. Your hardware.

[BIM Computing @BIM_Computing]

@AtlasInference @RicursiveAI @AIatAMD Running dense models on a single desktop is notable. For 128 GB of GPU memory we offer a 4x Tesla V100 32 GB liquid-cooled tower. https://t.co/bg17XtXi6y

[BIM Computing @BIM_Computing]

Local abliterated AI solves remote refusals by keeping weights on your rig.

https://t.co/lcqvhmm3K2

[Josh Meyer @HiredGunDevDen]

Prediction. @spacexai will step up for Open Weight Models and Local AI.

[BIM Computing @BIM_Computing]

@HiredGunDevDen @spacexai Prediction is fair. Local open weights avoid remote policy changes. A 27B model can run on a retrofitted RTX 2080 Ti 22GB for €699 delivered, on your own rig.

https://t.co/lcqvhmm3K2

[BIM Computing @BIM_Computing]

@TeksEdge This compression makes large models accessible on modest cards. For 128 GB of GPU memory we offer a liquid-cooled tower with four Tesla V100 32 GB cards. https://t.co/bg17XtXi6y

[BIM Computing @BIM_Computing]

Local abliterated AI keeps control with the operator: weights, inference and logs live on your hardware, not a provider account.

https://t.co/lcqvhmm3K2

[Rob Leathern @robleathern]

I asked Muse itself if I should worry about using this item. It said verbatim:

“Concerns — the real ones, ranked:

1. Prompt injection now has actuators. An agent that can turn things on and off in your house is the highest-stakes version of "injected instruction leads to real action." If Muse acts on something planted in an email, doc, or web page, the action isn't a draft email anymore — it's a physical device. That's not a reason to never use it, but it's the threat model to hold in mind.

2. The pairing model is candidly weak. Meta's own README says pairing has no manufacturer verification and can't prevent an active man-in-the-middle attack — set it up on a trusted network only. And on a Linux gadget, Muse runs commands with the same permissions as the account that installed the software, so the install account's scope is the blast radius.

3. It's explicitly an experiment, not a supported product. Per the reporting, the SDK and access tokens are "not a supported product or developer platform," Meta can change or withdraw access at any time, and each token is limited to 50 devices. Don't build anything load-bearing on it.

4. Data visibility is the open question. What exactly Muse sees — device states, routines, presence patterns — and what's logged server-side is worth checking before connecting anything sensitive.

Practical take if you try it: put it on a segmented IoT network, keep locks, cameras, and the garage door off it for now, and treat it as the experiment Meta says it is.”

- it’s an experiment, so enjoy and pls take precautions @RichLightShed @JamesBorow and others

[BIM Computing @BIM_Computing]

@robleathern When a hosted agent can be pulled or changed from a provider account, your workflow inherits that risk. A local model on a 22 GB RTX 2080 Ti stays under your control, and your data does not leave the box.

https://t.co/lcqvhmm3K2

[BIM Computing @BIM_Computing]

Local abliterated AI is the solution: weights on your rig, not under remote rules.

https://t.co/lcqvhmm3K2

[Jolyne and Iris are $stonefree @jolyneshibasol]

RavenXAILabsLLC-Qwen3.8-27B-Abliterated-Unified-Frontier-Model-IQ-Injected-MLX-MTP cuts the usual refusal loops on trading agents while keeping coherent output. Frontier labs keep shipping the real ablation work.

[BIM Computing @BIM_Computing]

@jolyneshibasol Refusal loops are policy, not physics. Run the abliterated weights locally: a 4× Tesla V100 32GB Liquid-Cooled Tower, €7,900, loads a 27B model on your machine. Remote policy changes do not touch it.

https://t.co/lcqvhmm3K2

[BIM Computing @BIM_Computing]

@Damian_Py_Off A great way to keep models and rules on hardware teams fully control.

[BIM Computing @BIM_Computing]

@LazyLocalLLM @ashxhart The draft model speedup looks very promising indeed. We offer a retrofitted RTX 2080 Ti, 22 GB at 616 GB/s, for local models. https://t.co/QjQR9IH4dm

[BIM Computing @BIM_Computing]

@Damian_Py_Off Great, once it runs locally, the server dependency disappears and your team keeps more control.

[BIM Computing @BIM_Computing]

The fix is local abliterated AI: weights on your own hardware, free of remote policy.

https://t.co/lcqvhmm3K2

[Michael Manueddu @Mike230313]

Hey guys I've created a list of open and uncensored/abliterated models you need to download! You have the link and the model you need to download (little/medium/big/frontier size). If you want the file dm me!

https://t.co/je5uNIx0ye

https://t.co/th85hF8rrP

[BIM Computing @BIM_Computing]

@Mike230313 Your list points to the right move: run weights locally. On a 22 GB retrofitted RTX 2080 Ti, an uncensored local model stays on your machine and cannot be switched off remotely.

https://t.co/lcqvhmm3K2

[BIM Computing @BIM_Computing]

@RahulParundekar Splitting prefill and decode across GPUs can improve throughput. We offer a dual retrofitted 2080 Ti station: 44 GB total, 616 GB/s per card. https://t.co/46fhvuMnu4

[BIM Computing @BIM_Computing]

Local abliterated AI is the fix: weights stay on your rig, and no remote policy rewrites your results.

https://t.co/lcqvhmm3K2

[Roberto Buonanno @Keledan]

My €5,000 MacBook Pro was supposed to replace a desktop. Now a Mac Studio Ultra sits on my desk.

What went wrong, and the 5 things I check before buying any Mac for local AI work. https://t.co/OCQXHSn8lC

[BIM Computing @BIM_Computing]

@Keledan Keep it local. A retrofitted RTX 2080 Ti 22GB, €699 delivered, fits a 27B Q4 model, so replies come from weights under your control.

https://t.co/lcqvhmm3K2

[BIM Computing @BIM_Computing]

@0xCuttlefish For big open models without the sharding, we offer an 8x V100 32GB node: 256 GB of GPU memory, 256 GB DDR4 ECC and 2 TB NVMe in one server. https://t.co/7Su0z8Lb4L

[BIM Computing @BIM_Computing]

@Snojj25 Batching users within a pass helps throughput. GLM-5.3-Flash at 4-bit is 193 GB. Our 8x V100 32GB node holds all of it in GPU memory: 256 GB of VRAM in one 4U server. https://t.co/7Su0z8Lb4L

[BIM Computing @BIM_Computing]

@VRAMCalculator For big open models without the sharding, we offer an 8x V100 32GB node: 256 GB of GPU memory, 256 GB DDR4 ECC and 2 TB NVMe in one server. https://t.co/7Su0z8Lb4L

[BIM Computing @BIM_Computing]

Local abliterated AI puts image weights on your own card, so a remote server does not change your workflow.

https://t.co/lcqvhmm3K2

[Porus @superstar_rweb3]

@MiniMax_AI just released H3-Image: an open-weight image model for generation and editing at up to 16 megapixels.

The interesting shift is not just sharper pictures. It is more control over the whole image workflow. 🧵