The best value in AI hardware isn’t new — it’s used. A used RTX 3090 with 24GB VRAM costs $400-500 and runs the same models as a $1,600 RTX 4090. Here’s what to buy and what to avoid.
Best used GPUs for AI
RTX 3060 12GB — Best budget ($200-300)
The entry point for GPU-accelerated AI. 12GB VRAM runs models up to ~14B parameters.
- VRAM: 12GB GDDR6
- Models: Qwen3.5-9B, DeepSeek Coder V2 Lite, MiMo-V2-Flash (tight)
- Speed: ~20-30 tok/s on 9B models
- Power: 170W TDP
- Why buy: Cheapest way to get GPU inference. Massive upgrade over CPU-only.
RTX 3090 24GB — Best value ($400-600)
The sweet spot. 24GB VRAM matches the RTX 4090 on capacity and runs all the best open-source models.
- VRAM: 24GB GDDR6X
- Models: Qwen 2.5 Coder 32B, Qwen3.5-27B, Codestral, everything up to ~32B
- Speed: ~25-35 tok/s on 32B models
- Power: 350W TDP (needs a beefy PSU)
- Why buy: 24GB VRAM at half the price of a 4090. The best deal in AI hardware.
RTX 3080 Ti 12GB — Avoid for AI
Same 12GB as the 3060 but costs more used. The extra CUDA cores don’t help much for AI inference — VRAM is the bottleneck. Buy a 3060 instead and save $100+.
RTX 4090 24GB — Best performance ($1,200-1,600 used)
If you want the fastest consumer GPU, a used 4090 is ~$400 cheaper than new. Same 24GB VRAM as the 3090 but significantly faster inference.
- Speed: ~45 tok/s on 32B models (vs 3090’s ~25-35)
- Power: 450W TDP
- Why buy: If speed matters more than budget.
Tesla A100 40GB/80GB — Enterprise ($2,000-4,000 used)
If you need more than 24GB VRAM, used A100s from decommissioned data centers are the best option.
- 40GB version: runs 70B models, ~$2,000 used
- 80GB version: runs nearly anything, ~$3,500 used
- No display output (headless server card)
- Needs a server chassis or workstation with the right cooling
What to avoid
- Any GPU with less than 8GB VRAM. Too small for useful models.
- RTX 3080 10GB. Awkward VRAM size — too small for 14B models, too expensive vs 3060.
- Mining GPUs. Cards that were used for crypto mining 24/7 have reduced lifespan. Check for signs of heavy use.
- AMD GPUs. ROCm support is improving but still has compatibility issues with many AI tools. Stick to NVIDIA.
Where to buy
- eBay: Largest selection, buyer protection. Filter by “used” and sort by price.
- Facebook Marketplace: Often cheaper than eBay, but no buyer protection. Meet locally.
- r/hardwareswap: Reddit’s hardware trading community. Good deals, community reputation system.
- Refurbished from manufacturers: NVIDIA and partners sometimes sell refurbished cards with warranty.
What to check before buying
- VRAM size. This is the #1 spec that matters for AI. More VRAM = larger models.
- Seller reputation. Check feedback scores and history.
- Mining history. Ask if the card was used for mining. Miners often ran cards at high temperatures 24/7.
- Physical condition. Check for bent pins, damaged fans, thermal paste condition.
- Test it. Run a benchmark (GPU-Z, FurMark) immediately after receiving. Return if performance is below spec.
- Power supply. The 3090 needs 350W and two 8-pin connectors. Make sure your PSU can handle it.
The recommendation
| Budget | Buy | What it runs |
|---|---|---|
| $200-300 | Used RTX 3060 12GB | 9-14B models, great entry point |
| $400-600 | Used RTX 3090 24GB | 27-32B models, best value |
| $1,200-1,600 | Used RTX 4090 24GB | Same models as 3090, 40% faster |
| $2,000+ | Used A100 40GB | 70B models, enterprise grade |
For most people: used RTX 3090 for $400-500. It’s the best deal in AI hardware and will remain useful for years.