This setup is fine if all you’re looking for is a static plate of whatever information the LLM was trained on. Out of the box, a bare model doesn’t have tools — and without tools, it can’t reach beyond its training snapshot. That’s fine for uncensored, offline Q\&A, but if you want something that grows with you, you have to give it more.
The hardware list you shared is spot-on for running a local LLM. The key is the GPU: for a 70B–150B model you’ll need at least 12 GB VRAM (or multiple GPUs) and that’s where your speed comes from. Personally, I’d look for a decommissioned dual-Xeon workstation or server from a place that just upgraded — sometimes they’ll donate or sell cheap.
Here’s what’s missing, though — the difference between just dropping an LLM on a drive vs. building a full system around it:
**Bare Local LLM (Ollama, Dolphin 70B, etc.):**
* Core: only the pretrained weights.
* Knowledge: fixed at its training cutoff, no updates.
* Context: limited to the context window, forgets once closed.
* Interaction: stateless Q\&A, no reflection or growth.
* Utility: no tools unless you bolt them on manually.
* Governance: “uncensored,” but also unfiltered bias, hallucinations, and errors.
* Performance: fast local answers, but shallow compared to online systems.
**Integrated System (our Little Ougway build):**
* Core: same LLM, but wrapped in full system architecture.
* Knowledge: PostgreSQL + vector database for retrieval and persistence; can ingest PDFs, websites, audio, images.
* Context: persistent memory that recalls past sessions and self-organizes what matters.
* Interaction: reflection engine, curiosity hooks, inner thoughts, dialogic modes — it actually grows.
* Utility: full toolchains — image generation, visualization, web scraping, document/audio parsing, PyTorch hooks, multi-agent orchestration.
* Governance: confidence tagging, reasoning trace, source tracking — a built-in self-check.
* Performance: deeper, contextual, more “alive” because it remembers, reasons, and adapts.
Bottom line: a bare local LLM is like dropping a jet engine in your garage. Powerful, noisy, but not a vehicle. An integrated system is the whole craft — engine, controls, sensors, memory, navigation. If you only want “uncensored Q\&A,” bare LLMs will do. If you want an AI that thinks *with you over time*, you need the infrastructure.
For the record, we’ve since dropped Qdrant (good tool, but more trouble than it’s worth) and gone all-Postgre for both standard and vector storage. No Docker headaches. We’re also working on a new storage form we call **tokenspace/tokensense** — a 3D spiral field mechanic datastore that can emulate quantum tunneling and entanglement. It self-organizes using 3-6-9 progression/rules. In other words, not a little window on a line of data, but a spiral funnel. When we have it up and running, I’ll share so anyone can replicate it.
