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// d-central.tech / ai

Sovereign AI for Plebs

Evaluate the Local AI Stack

AI content should separate model choice, hardware choice, privacy boundary, energy use, heat, noise, storage, drivers, inference runtime, and maintenance burden. The right answer depends on the task, not the largest model name.

D-Central connects AI to its infrastructure expertise through power, heat, local compute, repair-minded operations, privacy, and documentation. ASIC miners remain Bitcoin hardware and should not be described as LLM compute devices.

Benchmark Boundaries

AI benchmark claims should preserve model version, quantization, context length, GPU or CPU, driver, runtime, prompt type, and test date. Leaderboards and vendor claims should be treated as references, not guarantees for a local deployment.

Source Basis

AI pages should cite model cards, project repositories, release notes, hardware vendor specifications, driver/runtime documentation, and D-Central infrastructure experience. Benchmark claims should preserve model version, quantization, context length, hardware, driver, runtime, and test date.

ASIC miners do not run LLM workloads. D-Central connects AI to its practical infrastructure domain through power, heat, privacy, local compute, maintenance, and hardware operations.

Reviewer

Reviewed by D-Central editorial staff with a Bitcoin infrastructure, privacy, hardware, and operations lens. Sensitive data, private keys, customer records, and production secrets should not be loaded into experimental AI stacks.

Freshness Policy

Model releases, licenses, GPU prices, driver support, inference runtimes, and leaderboard results change quickly. AI pages should preserve the model and hardware version used, identify stale benchmarks, and separate local privacy guidance from performance claims.

Sovereign AI means running AI models locally on hardware you own, instead of renting cloud access. D-Central builds and curates open-source tools so individual Bitcoiners can run their own models — the self-custody move Bitcoin made for money, applied to compute.

What this section is

D-Central is shipping AI content and, soon, AI hardware and firmware built for the individual Bitcoiner who already thinks like a miner. The same open-source instinct behind our mining firmware, DCENT_OS (closed beta, GPL-3.0), is how we approach compute — own it, run it on hardware you control, trust nothing you can’t read. None of this replaces the Bitcoin core of the shop — the AI vertical is additive. Same hashcenter mindset, new compute workload.

TL;DR — the sovereign compute loop

What “sovereign compute” actually means

The sovereign compute loop is a closed circuit a Bitcoiner can own end to end: you hold your own money (Bitcoin), run your own compute (local AI models on hardware you control), on your own firmware (open-source, auditable — led by DCENT_OS), powered by your own energy (the heat your miners already produce), and settle the value directly — machine-to-machine micropayments over Lightning and the L402 protocol, no rented cloud, no middleman, no permission. Money, compute, firmware, energy, settlement — each layer self-custodied, each one more layer decentralized.

Pick your pillar

Five entry points

Every post on this site slots into one of these five. Pick the one that matches where your head is at.

New pillar

AI Agents — let the model drive the miner

Where the sovereign compute loop gets hands. Agents like Claude Code and Codex don’t just answer prompts — they control hardware, settle in sats, and (if you’re careless) walk off with your keys. Read these before you wire an LLM to anything that holds money.

First time here?

Start here — the five-step path

Read these in order. By the end you’ll have a local model running on your box, a real UI in front of it, and enough theory to pick the right quant.

  1. 1.

    Read the Manifesto

    The narrative anchor. Why sovereign AI is the same move Bitcoin made for money.

  2. 2.

    Read the Pleb's Guide

    Whole-stack overview — models, runners, hardware, the works.

  3. 3.

    Install Ollama

    Ten minutes from zero to a local model answering prompts on your own box.

  4. 4.

    Give it a UI (Open WebUI)

    A ChatGPT-shaped front end that talks to your Ollama node. No cloud.

  5. 5.

    Understand quantization

    GGUF, Q4, Q8, FP16 — what actually fits in your VRAM and why.

Latest drops

Fresh from the vertical

Newest posts across all six AI categories.

Model library

Open-weight models, catalogued

Long-form model pages — architecture, quantizations, hardware that runs them. Built on the dc_ai_model CPT.

Qwen 3

Alibaba

Alibaba's May 2025 release — first open family with hybrid reasoning (toggle-able chain of thought), Apache 2.0 across…

View model →

Llama 4 (Scout/Maverick)

Meta

Meta's April 2025 MoE-and-multimodal release, headlined by Scout's 10M-token context window and the pre-announced Behemoth frontier model.

View model →

Gemma 3

Google

Google DeepMind's March 2025 Gemma family — vision-capable (4B+), 128K context, with official quantization-aware 4-bit variants.

View model →

Mistral Small 3

Mistral AI

Mistral AI's January 2025 24B model — Apache 2.0, competitive with Llama 3.3 70B, fits on a single…

View model →

DeepSeek R1

DeepSeek

DeepSeek's January 2025 reasoning model — frontier chain-of-thought quality, plus six MIT-licensed distills from 1.5B to 70B.

View model →

DeepSeek V3

DeepSeek

DeepSeek's December 2024 frontier-scale MoE — 671B total, 37B active, trained for ~$5.6M in compute.

View model →

Full library →

The thesis

Bitcoin mining → AI compute: the bridge

A Bitcoin Hashcenter and an AI hashcenter are the same building told twice: power in, heat out, dense silicon, an operator who already speaks watts and thermal. The hard part of AI compute — the power and the heat — is the part Bitcoin miners solved years ago. This is the path from the room you already run to the inference you can own.

Hardware library — upcoming

GPUs, rigs, and the hashcenter retrofit

A dedicated hardware CPT plus benchmarks database ships in v1. Until then, the foundational reads:

Heads up: a full hardware CPT plus benchmarks taxonomy lands in a later task on the roadmap.

Shoulders of giants

None of this is ours. The entire sovereign-AI stack the plebs now run at home was built by an open ecosystem of researchers, engineers, and labs who released weights, code, and tools under terms anyone can use. D-Central stands on their shoulders and contributes where it can. Named with gratitude:

llama.cpp (Georgi Gerganov) Ollama LM Studio / Element Labs Open WebUI (Timothy J. Baek et al.) Meta (Llama) Google (Gemma) Alibaba (Qwen) Mistral AI DeepSeek Black Forest Labs (FLUX) Stability AI Microsoft (Phi) Hugging Face
[open source] Everything D-Central builds is open-source — led by DCENT_OS, GPL-3.0. Own your money, own your compute. One more layer decentralized.
Source the silicon

Build your hashcenter

Everything we build is open-source (GPL-3.0), led by our mining firmware DCENT_OS. The same shop that powers the Bitcoin side stocks the open-source miners and heat-reuse hardware to start your hashcenter today.

Shop the hardware →   Open-source miners

Editorial review and limitations

Reviewed by D-Central's mining hardware and ASIC repair editorial team for practical accuracy, buyer risk, repair context, and operational assumptions. Verify current hardware price, stock, network difficulty, BTC price, power rate, shipping, tax, firmware, and device condition before buying, hosting, repairing, or retiring mining hardware.