Decision Guide: Local LLM Deployment Options¶

Version: 1.01.26
Audience: Consultant / Technician
Purpose: Quickly choose the most appropriate SOP for a given client scenario.

1. Quick Flow (Question-Driven)¶

Use this simplified decision path during discovery calls or internal planning.

Q1. What is the primary purpose?
    ├─ A: Automation / Scheduling / Batch tasks  → go to Q2
    ├─ B: Personal desktop assistant / notes     → go to Q3
    ├─ C: General reasoning / Q&A only           → go to Q3
    └─ D: Mixed / Not sure                       → go to Q3

Q2. Advanced Automation needed (cron / "every X hours")?
    ├─ Yes → Recommend SOP #4 (Goose/Other + n8n/Other + LLM + Agent containers)
    └─ No  → Continue at Q3

Q3. Is the data extremely sensitive?
    (e.g., Secret-class, doctor–patient, lawyer–client, privileged legal, PHI)
    ├─ Yes → go to Q4
    └─ No  → go to Q5

Q4. For extremely sensitive data:
    ├─ UI required? 
    │    ├─ Yes → Prefer SOP #3 (LM Studio Local Runner)
    │    └─ No  → Prefer SOP #2 (Terminal-Only LLM Container)
    └─ Goose may only be used if fully firewalled and approved by compliance.

Q5. Who will maintain it day-to-day?
    ├─ Technician / IT or comfortable with Docker → go to Q6
    └─ Non-technical / wants max simplicity   → Prefer SOP #5 (Goose Standalone)

Q6. What interaction style is preferred?
    ├─ Desktop UI (chat) but still want containerization
    │       → SOP #1 (LLM Container + Goose UI on host)
    ├─ Terminal / IDE/ Script integration
    │       → SOP #2 (Terminal-Only LLM Container)
    └─ Desktop UI without containers
            → SOP #3 (LM Studio) or SOP #5 (Goose Standalone)

Q7. Hardware capability (VRAM/CPU)?
    ├─ < 8 GB VRAM → prefer lighter 3B–7B model, all SOPs still possible but keep load low
    ├─ 8–12 GB     → 7B–8B models ideal; SOP #5 or #3 recommended for non-technical users
    ├─ 12–24 GB    → 8B–14B models viable; container-based SOP #1/#2/#4 become attractive
    └─ > 24 GB     → 14B+ models and heavy workloads; any SOP valid, choose by UX/security

2. SOP Mapping Summary (One-Line Purpose)¶

SOP #1: LLM Container + Goose UI → Local containerized model with a desktop UI for reasoning-focused workflows.
SOP #2: LLM Container, Terminal Only → Minimal, privacy-focused, CLI-driven local inference.
SOP #3: LM Studio Local Runner → Simplest “local ChatGPT” for a single user, no containers.
SOP #4: Goose + n8n + LLM+Agent Containers → Local AI automations and scheduled tasks with UI.
SOP #5: Goose Standalone (Windows) → One-app local assistant for non-technical users, no Docker.

3. Comparison Grid¶

Factor / Question	SOP #1: LLM+Goose (Container)	SOP #2: Terminal Only	SOP #3: LM Studio	SOP #4: Goose + n8n + Agent	SOP #5: Goose Standalone
UI Needed	Yes (Goose)	No	Yes (LM Studio)	Yes (Goose)	Yes (Goose)
Automation / Scheduling	Limited (scripts)	Limited (cron/scripts)	No built-in	Yes (n8n)	No
Docker Required	Yes	Yes	No	Yes (two stacks)	No
Best For	Reasoning with UI + isolation	High-privacy + CLI + Devs	Simple local chat	Complex workflows / cron	Non-savvy users
Extreme Sensitive Data (Secret / HIPAA / Legal)	Only with firewall + review	Preferred (no UI)	Preferred if UI	Generally not preferred	Generally not preferred
Hardware Complexity	Medium	Medium	Low	High	Low
User Skill Assumed	Technician + UI user	Technician only	End-user	Technician + power user	End-user (minimal)

4. Quick Recommendations by Scenario¶

Client wants a “local ChatGPT” and nothing more:
→ Default: SOP #3 (LM Studio) or SOP #5 (Goose Standalone on Windows).
Client wants manageable local AI + simple UI + some safety:
→ SOP #1 (Container + Goose UI) if technician support is available.
Client wants maximum privacy, intends to use in an IDE, or is okay with terminal:
→ SOP #2 (Terminal-Only LLM Container).
Client wants “every X hours do Y” or "IFTTT" automation:
→ SOP #4 (Goose + n8n + LLM+Agent containers).
Client has little technical skill but wants powerful local AI on Windows:
→ SOP #5 (Goose Standalone).