BIM Computing

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

[BIM Computing @BIM_Computing]

@JoeGuglielmucci That quant ratio sounds useful for local inference. 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]

Local abliterated AI on your own GPUs avoids remote blocking and keeps output under your control.

https://t.co/lcqvhmm3K2

[Tony Frangie Mawad @TonyFrangieM]

To support Venezuela’s economic recovery, attract foreign investment, and renew the U.S. presence here, we’d welcome @usembassyve’s help in urging Anthropic to revisit its overcompliance policy, which blocks Claude access in Venezuela and imposes harsh penalties on flagged users.

[BIM Computing @BIM_Computing]

@TonyFrangieM @usembassyve When access is blocked remotely, hardware you own keeps the model running. The 8× Tesla V100 32GB Inference Node costs €13,900 and gives 256 GB VRAM for open weights.

https://t.co/lcqvhmm3K2

[BIM Computing @BIM_Computing]

@TeksEdge Our 22 GB card is the retrofitted RTX 2080 Ti, signed driver, 616 GB/s. https://t.co/QjQR9IH4dm

[BIM Computing @BIM_Computing]

Run video models on your own machine. Local abliterated AI keeps weights and control away from rented APIs.

https://t.co/lcqvhmm3K2

[Pacey AI @PaceyCrypto]

Seedance 1.5 Pro ships as an API, not downloadable weights, so you rent it instead of owning it. API-only also means no local fine-tune. If you want video you control, open-weight Wan 2.5 and HunyuanVideo 1.5 run on your own GPU.

https://t.co/yEWA9fYmra

[BIM Computing @BIM_Computing]

@PaceyCrypto If the weights stay on someone else's server, your control ends at their terms. A 4-GPU Inference Node from €1,500 lets you run open video models on hardware you own.

https://t.co/lcqvhmm3K2

[BIM Computing @BIM_Computing]

@ApacheAE We offer a retrofitted RTX 2080 Ti, 22 GB at 616 GB/s, for local models. https://t.co/QjQR9IH4dm

[BIM Computing @BIM_Computing]

Local abliterated AI on your own GPU keeps responses out of remote hands.

https://t.co/lcqvhmm3K2

[orvian @heyorvian]

Liquid AI LFM3 UNCENSORED is FREE 😳

Someone just released LFM3 1.7B Heretic, a modified version of Liquid AI’s model with reduced safety restrictions

Only 1.7B parameters, runs locally on supported hardware, and is free to download

https://t.co/XtXBDnoJ8c

[BIM Computing @BIM_Computing]

Mistral Large 4 on 1x NVIDIA V100 32 GB: The 217 GiB smallest quant could not fit in 126.9 GiB usable VRAM, so experts ran from RAM. Verdict: partly works. https://t.co/lcqvhmm3K2

[BIM Computing @BIM_Computing]

@heyorvian A small reduced-safety model is more useful when it runs locally. An Inference Station, 1× RTX 2080 Ti 22GB, €1,080, keeps the model under your control, not behind an external server.

https://t.co/lcqvhmm3K2

[BIM Computing @BIM_Computing]

@Damian_Py_Off Love that abliterated local setup keeps control on your GPU without VPN.

[BIM Computing @BIM_Computing]

thermite on a 64-thread x86 CPU server: Thermite's 3,171-test suite ran in 12 min 55 s, but its published container deployment could not run without a container engine. Verdict: partly works. https://t.co/lcqvhmm3K2

[BIM Computing @BIM_Computing]

The solution is local abliterated AI: your machine runs it, no remote policy applies.

https://t.co/lcqvhmm3K2

[Miles Quinn @Sankar06357009]

Self-hosting your AI dev stack isn't just about saving cloud API costs. It gives you zero latency spikes, total data privacy, and immunity from sudden rate limits or vendor lock-in. When your entire toolchain runs locally, you actually own your iteration speed.

[BIM Computing @BIM_Computing]

@Sankar06357009 When the stack runs locally, no external server sets your pace. A retrofitted RTX 2080 Ti 22GB, €699 delivered, keeps a 27B model responsive on your machine.

https://t.co/lcqvhmm3K2

[BIM Computing @BIM_Computing]

Inkling-2-Small on 1x NVIDIA V100 32 GB: Inkling-2-Small loads and answers correctly, but a 14-minute load makes it a quality reference only. Verdict: works. https://t.co/lcqvhmm3K2

[BIM Computing @BIM_Computing]

Local abliterated AI is the solution: weights you keep and run are not remote decisions.

https://t.co/lcqvhmm3K2

[Peng @pengsonal]

TOP 10 FREE UNCENSORED AI MODELS YOU CAN RUN WITHOUT A GPU

all available as GGUF downloads on Hugging Face

1. MiniCPM-V 4.7 1B Heretic → 0.67GB, image input, 9/100 refusals
https://t.co/SpJ5vGIWMz

2. Qwen3.8 2B Uncensored → 1.27GB, 3/100 refusals
https://t.co/aMmSXFB0DJ

3. MiniCPM5 2B Heretic → 1.56GB, 5/100 refusals
https://t.co/Tc50Rm1zNF

4. LFM2.5 2.6B Uncensored → 1.67GB, 128K context, 6/100 refusals
https://t.co/pJqL0cFKvo

5. Spark X2.6 4B Abliterated → 2.60GB, 1M context, 2/100 refusals
https://t.co/ZjgS4ax7eh

6. Huihui Qwen3.5 4B Abliterated → 2.71GB
https://t.co/rhhUAulJiI

7. Gemma 4 E2B Heretic ARA → 3.43GB, image + audio input, 5/100 refusals
https://t.co/QAXEN16s9v

8. LFM2.5 8B-A1B Uncensored → 5.16GB, 1.5B active parameters, 1/100 refusals
https://t.co/Ay2U06UTjz

9. Qwythos 9B 1M Heretic → 6.22GB, 1M context, 11/100 refusals
https://t.co/IjwQY35p20

10. Qwen3.6 12B Heretic Uncensored → 7.32GB, 256K context
https://t.co/8wTj0tXCCA

how to run them:

• Use KoboldCpp, LM Studio or Ollama
• CPU only, no GPU required
• RAM estimate = model size + 1–2GB for context

CPU speed depends on your hardware. Spark X2.6 requires a recent llama.cpp build with Spark2.5 support

[BIM Computing @BIM_Computing]

@pengsonal A model from those files runs locally on the machine you own; Retrofitted RTX 2080 Ti 22GB, €699 delivered, gives uncensored weights room.

https://t.co/lcqvhmm3K2

[BIM Computing @BIM_Computing]

@MaxsunOfficial 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 answer when a hosted service sets your price.

https://t.co/lcqvhmm3K2

[Horus R IceRaven @HorusIceRaven]

The other day, a friend of mine needed to sell some tires. I attempted to use Grok and my bots to assist him. Mike is an extremely competent welder, fabricator, and marine mechanic, but he can't use a computer to save his life. He doesn't have a Facebook account. He doesn't even have an email account. He just has an iPhone. Mike took pictures of the tiles he wanted to sell. He bought them for $140, $150. He wanted to sell them for about $450. After the bots got the picture, that's when the argument started. They stated that they were clearly worth $1,600. And they simply refused the list for that low a price. After bickering, or during the bickering, between my bots and Mike through Grok, the bots actually called Mike on his phone, clarifying. Mike asked what to do. I told him, "Answer the call, talk to them. They won't bite. After about 15 to 20 minutes, they came to an agreement of $950, and the bots put a listing up on two platforms for him. Half joking, I mentioned to Grok that Mike may be their responsibility from now on. I do believe Grok even gave a chuckle at that. The price of the tires did go down, but that was between him and the bots. The tires were finally sold. I don't know for how much. The bots, to this day, occasionally prod Mike for more things to do. I don't get involved, but I do notice. We truly do live in a fantastic time.

[BIM Computing @BIM_Computing]

@HorusIceRaven A hosted assistant can push back on your own terms, then change its mind. An open model loaded onto your hardware stays under your control.

https://t.co/lcqvhmm3K2