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Cheapest Way to Run AI Locally (Under $300)

Run AI models locally for under $300. Used office PCs, Raspberry Pi 5, and budget CPU-only setups that actually work for local LLM inference.

Last updated: February 7, 2026

🎯 Why This Matters

You don't need a $1,600 GPU to run AI locally. A used office PC with 32GB RAM can run 7B models at usable speeds. Even a Raspberry Pi 5 can run tiny models. The cheapest path to local AI is CPU inference with enough RAM — it's slower than GPU, but it's private, free, and surprisingly capable for simple tasks.

🏆 Our Recommendations

Tested and ranked by real-world AI performance

💚 Budget

Used Dell OptiPlex / HP EliteDesk (32GB RAM)

$150-200
SpecsIntel i5-10th/11th gen or Ryzen 5, 32GB DDR4, 512GB SSD, refurbished
Performance~8-12 tok/s with 7B Q4 (CPU), ~4-6 tok/s with 13B Q4
Best ForBudget-conscious users, first local AI experience, home server

✅ Pros

  • Incredibly cheap at $150-200 refurbished
  • 32GB RAM handles 7B-13B models
  • Quiet and low power
  • Great as always-on AI server
  • Upgrade path: add a GPU later

❌ Cons

  • CPU-only inference is slower
  • Limited to 7B-13B models comfortably
  • May need RAM upgrade
  • Older CPUs lack AVX-512 optimizations
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💚 Budget

Raspberry Pi 5 (8GB)

$80
SpecsBroadcom BCM2712, 8GB LPDDR4X, microSD/NVMe, 5W idle
Performance~2-3 tok/s with 3B Q4, ~1 tok/s with 7B Q2
Best ForTiny models, learning/experimentation, always-on assistant

✅ Pros

  • Only $80
  • 5W power consumption
  • Silent operation
  • Fun learning project
  • Always-on capable

❌ Cons

  • Very slow inference
  • Only 8GB RAM — limited to 3B models
  • ARM performance ceiling
  • Not practical for daily use with larger models
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💙 Mid-Range

Budget Build: Ryzen 5 + 32GB DDR5 + Used GPU

$250-300
SpecsAMD Ryzen 5 5600, 32GB DDR4, used GTX 1070 8GB or RX 580 8GB, 500GB SSD
Performance~15-20 tok/s with 7B Q4 (GTX 1070), ~6-8 tok/s with 13B Q4 (partial offload)
Best ForBest performance under $300, 7B models with GPU acceleration

✅ Pros

  • GPU acceleration makes 7B models fast
  • Upgradeable platform
  • Can game too
  • Good balance of cost and speed

❌ Cons

  • Requires assembly
  • Used GPU may have limited warranty
  • 8GB VRAM limits model sizes
  • Older GPU architecture
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💡 Prices may vary. Links may earn us a commission at no extra cost to you. We only recommend products we'd actually use.

🤖 Compatible Models

Models you can run with this hardware

DeepSeek R1 7B

7B

6 GB min VRAM · DeepSeek

Gemma 2 2B

2B

2 GB min VRAM · Google

Llama 3.2 1B

1B

2 GB min VRAM · Meta

Llama 3.2 3B

3B

3 GB min VRAM · Meta

Mistral 7B

7B

6 GB min VRAM · Mistral AI

Phi-4

14B

10 GB min VRAM · Microsoft

❓ Frequently Asked Questions

Can I really run AI for under $100?

Yes, but with limitations. A Raspberry Pi 5 ($80) can run 1-3B models at 1-3 tok/s. It's slow but works for simple Q&A. For a more practical experience, aim for $150-200 on a used office PC with 32GB RAM.

Is CPU inference actually usable?

For 7B models, absolutely. Modern CPUs with AVX2 support give 8-12 tok/s, which is like reading speed. For chat and coding assistance, that's perfectly usable. 13B models at 4-6 tok/s are slower but still workable.

What's the best first model on a budget setup?

Start with Llama 3.2 3B or Phi-4 (3.8B). They're small enough to run on almost anything with 8GB+ RAM, and surprisingly capable for general chat, summarization, and simple coding tasks.

Ready to build your AI setup?

Pick your hardware, install Ollama, and start running models in minutes.

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