58 lines
2.8 KiB
Markdown
58 lines
2.8 KiB
Markdown
# Variet LLM: Dual-Orchestration AI Assistant
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## What This Is
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A high-performance, locally-hosted AI assistant system built on two RTX 3060 12GB GPUs. It uses a "2+0" architecture where Machine A acts as a dedicated inference server running large language models, while Machine B handles the user interface (VS Code, Discord) and tool execution.
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## Problem / Core Value
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Standard LLM set-ups on a single GPU often struggle with context switching and running multi-tools asynchronously. By dedicating an API server to raw inference (50-80 t/s with Qwen 35B), the system achieves extreme responsiveness for coding while preserving resources for tool execution (Calendar, Mail, Search) on the workstation.
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## Target Audience
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Single developer working on complex coding tasks alongside daily administrative tasks.
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## Key Decisions
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| Decision | Rationale | Outcome |
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|----------|-----------|---------|
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| 2+0 GPU Architecture | Placing both GPUs in Machine A allows Qwen 35B to fully load into VRAM, increasing speed from 30t/s to 50-80t/s. | Machine A: API Server only.<br/>Machine B: All orchestrations & tools. |
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| Separation of Agent Logic | Machine A is a pure "brain" (llama-server). Machine B has the "hands and eyes" (VS Code extension and Discord Bot). | Simplified infrastructure; tools execute directly on the workstation. |
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| 3-Tier Model Strategy | Need balanced speeds depending on the complexity of the task requested. | Fast: Gemma4 26B (~70t/s)<br/>Balanced: Qwen 35B (~50t/s)<br/>Deep: Qwen 122B (~11t/s) |
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## Requirements
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### Validated
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(None yet — ship to validate)
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### Active
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- [ ] Deploy headless llama-server setup on Machine A.
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- [ ] Build a model hot-swap utility (Fast/Balanced/Deep) for Machine A.
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- [ ] Develop a VS Code Extension (TypeScript) on Machine B for coding agent loop.
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- [ ] Develop a Discord Bot (discord.py) on Machine B for personal assistant tools.
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- [ ] Implement MCP tools (SearXNG, Google Calendar, Gmail) securely on Machine B.
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### Out of Scope
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- [ ] Running inference directly on Machine B (It lacks VRAM/GPU resources in this architecture).
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- [ ] Exposing Machine A to the public internet (LAN traffic only).
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---
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*Last updated: 2026-04-05 after initialization*
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## Evolution
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This document evolves at phase transitions and milestone boundaries.
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**After each phase transition** (via `/gsd-transition`):
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1. Requirements invalidated? → Move to Out of Scope with reason
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2. Requirements validated? → Move to Validated with phase reference
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3. New requirements emerged? → Add to Active
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4. Decisions to log? → Add to Key Decisions
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5. "What This Is" still accurate? → Update if drifted
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**After each milestone** (via `/gsd-complete-milestone`):
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1. Full review of all sections
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2. Core Value check — still the right priority?
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3. Audit Out of Scope — reasons still valid?
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4. Update Context with current state
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