# LLMs Locally on Consumer Hardware — Part 10: How the Setup Evolved — A Timeline of What Changed and Why

> Source: <https://dev.to/sven_welack/llms-locally-on-consumer-hardware-part-10-how-the-setup-evolved-a-timeline-of-what-changed-and-24f7>
> Published: 2026-11-11 07:15:58+00:00

*Part 10 of a series — a retrospective appendix. The narrative ran across Parts 1–9; this one lays the evolution out stage by stage.*

No part of the setup was chosen for its own sake. Every change — a new machine, a different coordinator, a smaller model — was forced by a specific failure in the stage before it. Below is each stage as a snapshot, with the arrow between snapshots showing what broke and what was added or removed to fix it.

| Machine | What it runs | 
|---|---|
| Main — 5950X · ~80 GB · RX 6900XT | Coordinator **and** all workers — everything on`gemma4:26b` , local | 

⬇ **Broke:** `gemma4:26b` can't hold a coordination loop beyond ~1–2 delegations.

**Added:** a search for a dedicated coordinator model (workers stay on gemma).

| Machine | What it runs | 
|---|---|
| Main — ~80 GB · RX 6900XT | Coordinator: trialing `gpt-oss:120b` (and`groq-tool-use:8b` , which fabricated sessions) · Workers:`gemma4:26b` | 

⬇ **Broke:** the 120B (~77 GB) and the gemma workers (~20 GB) can't both be resident in 80 GB RAM → ~100 GB swapped per hand-off; 291 s to say hello.

**Removed:** the workers from the main machine · **Added:** a second machine (5600G + GTX 1060).

| Machine | What it runs | 
|---|---|
| Main — ~80 GB · RX 6900XT | Coordinator: `gpt-oss:120b` (resident; runs on CPU + RAM) | 
| Worker (.114) — 32 GB · GTX 1060 | Workers: `gemma4:26b` | 

⬇ **Noticed:** with the 120B running on CPU and RAM, the main machine's GPU sits idle.

**Added:** image generation (ComfyUI + SDXL) on the main 6900XT.

| Machine | What it runs | 
|---|---|
| Main — ~80 GB · RX 6900XT | Coordinator `gpt-oss:120b` (~77 GB RAM)**+** SDXL image gen (~15 GB VRAM) | 
| Worker (.114) | `gemma4:26b` workers | 

⬇ **Broke:** the 120B leaves only ~3 GB RAM free → SDXL generations time out.

**Removed:** `gpt-oss:120b` · **Added:** `gpt-oss:20b` (~14 GB) in its place.

| Machine | What it runs | 
|---|---|
| Main — ~80 GB · RX 6900XT | Coordinator `gpt-oss:20b` (~14 GB RAM)**+** SDXL (~15 GB VRAM) —**~60 GB RAM free** | 
| Worker (.114) | `gemma4:26b` workers | 

⬇ **Added over the same period:** a cloud strategist (Sage, on Gemini 3 Flash) for the one role needing deep reasoning; the laptop as an SD 1.5 image node; and deterministic orchestration (a script owns the delegation loop, the model only plans and evaluates).

| Where | What it runs | 
|---|---|
| Main — ~80 GB · RX 6900XT | Coordinator `gpt-oss:20b` + SDXL image generation | 
| Worker (.114) — 32 GB · GTX 1060 | `gemma4:26b` specialist workers | 
| Laptop (.247) — 32 GB DDR5 · 780M | SD 1.5 image node (512 px) | 
| Cloud | Sage strategist — Gemini 3 Flash (free tier), consulted when blocked | 

The 8840U laptop ran its own small arc inside the larger one.

| Attempt | Result | 
|---|---|
| Laptop 8840U / 780M as an **LLM worker** | CPU prefill ~25 tok/s → 13-minute stalls | 

⬇ **Why:** no discrete GPU, so prefill is hopeless for large agent prompts (Part 2).

**Repurposed** for the one job the hardware could do:

| Repurposed | Result | 
|---|---|
| Laptop as an **image node** | SDXL hangs on the 780M; SD 1.5 @ 512 px works (~37–42 s) | 

|  | Initial setup | Final setup | 
|---|---|---|
| **Machines** | 1 (main) | 3 (main, worker, laptop) + 1 cloud model | 
| **Coordinator** | `gemma4:26b` , local | `gpt-oss:20b` , local | 
| **Workers** | `gemma4:26b` , same machine as the coordinator | `gemma4:26b` , dedicated worker machine (.114) | 
| **Strategist** | — | Gemini 3 Flash, cloud (consulted only when blocked) | 
| **Image generation** | — | SDXL on the main 6900XT; SD 1.5 on the laptop node (.247) | 
| **Orchestration** | model-driven loop | deterministic script owns the loop; model only plans and evaluates | 

Read top to bottom, the chain has a shape. Nearly every change was one of two moves: **placement** — which machine a thing runs on — or **right-sizing** — how large each model needs to be for its job. The workers moved for placement; the coordinator shrank for right-sizing; the strategist went to the cloud because that was the right placement for the one role that needed deep reasoning. Not once did the answer turn out to be "buy a bigger model and hope." The capability was almost always already there in the hardware on hand; the work was in arranging it so each piece had room to do the thing it was good at.