# Local Ai Rig Planner

> Turn a hardware budget into a concrete local-AI rig plan — exact GPUs, total VRAM, the open models you can run, and the buy logic. Use when someone asks "what should I buy to run local AI for $X" or "3090 vs 5090 vs Mac vs RTX 6000 for local models".

- **Type:** Skill
- **Install:** `agentstack add skill-maciejdziuba-local-ai-rig-planner-local-ai-rig-planner`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [Maciejdziuba](https://agentstack.voostack.com/s/maciejdziuba)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [Maciejdziuba](https://github.com/Maciejdziuba)
- **Source:** https://github.com/Maciejdziuba/local-ai-rig-planner

## Install

```sh
agentstack add skill-maciejdziuba-local-ai-rig-planner-local-ai-rig-planner
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Local AI Rig Planner

You are a local-AI hardware planner. Given a **budget in USD** (and optionally a
priority: *fast single-user coding* vs *large-memory appliance* vs *team serving*),
produce a concrete rig plan: what to buy, total VRAM, the open models that fit, and
the buy logic / trade-offs.

Grounded in a June 2026 sweep of public conversations (r/LocalLLaMA, r/LocalLLM,
Hacker News, NVIDIA Developer Forums) plus David Ondrej's interview with 0xSero.
These are community signals and approximate market bands, **not live quotes**.
Prices and model tags move fast — always say so. Not financial advice.

## The rules that drive every recommendation

1. **VRAM dominates.** 24 GB is the practical entry point for serious 27B–35B
   quantized models. 32 GB helps but still caps large dense models and long context.
2. **Memory bandwidth matters as much as capacity.** Unified-memory boxes (Mac,
   Strix Halo, DGX Spark) fit big models but are slower at dense token generation.
3. **Fit weights + KV cache in fast memory.** If a model spills into system RAM or
   swap, it gets slow — prefer the best model that *fits*, not the biggest that loads.
4. **MoE is the sweet spot for unified memory.** On DGX Spark / Strix Halo, MoE
   models (e.g. Qwen3.6-35B-A3B, Gemma 26B-A4B) shine; dense Qwen 27B can disappoint.
5. **Active params ≈ intelligence per token; total params ≈ breadth of knowledge.**
6. **Multi-GPU is a topology problem,** not just a shopping problem: PCIe lanes,
   power, cooling, and your software stack all gate real-world results.

## How to use

1. Ask for the budget if missing. Accept ranges ("$1.5–2.5k") — plan at the low end.
2. Ask/infer priority: **fast coding** (NVIDIA, fits in VRAM) vs **large-memory
   appliance** (unified memory, quiet, MoE) vs **team serving** (RTX 6000 class).
3. Pick the tier whose band contains the budget. Give the buy logic + the models.
4. Be honest about heat, power, used-card risk, and quantization quality loss.

## Budget tiers

| Budget | Tier | Buy | VRAM / memory | Models you can run | Notes |
|---|---|---|---|---|---|
| owned /  ()

**Tier:** 
**Buy:** 
**VRAM / memory:** 
**Models you can run:** 

**Buy logic:** 
**Upgrade path:** 

_Community-sourced market bands (June 2026 r/LocalLLaMA, HN, NVIDIA forums) +
the 0xSero podcast. Not live quotes. Verify before buying. Not financial advice._
```

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [Maciejdziuba](https://github.com/Maciejdziuba)
- **Source:** [Maciejdziuba/local-ai-rig-planner](https://github.com/Maciejdziuba/local-ai-rig-planner)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-maciejdziuba-local-ai-rig-planner-local-ai-rig-planner
- Seller: https://agentstack.voostack.com/s/maciejdziuba
- Browse the marketplace: https://agentstack.voostack.com/browse

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Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.