GPU Value Finder for Local AI
Use this when the question is value, not just fit: for your budget and the model size you want to run, which catalog GPU gives the most VRAM for the money. The ranking is interactive, but its internal planning prices are a dated snapshot; confirm current price and availability before buying.
GPU Value Finder
Find the best memory-per-dollar GPU for your budget and the model size you want to run. We rank the cards; you check the live price on Amazon before buying.
Ranked from an internal price snapshot reviewed 2026-07-11. GPU prices move fast, so always confirm the current listing before relying on the rank or budget filter.
| GPU | VRAM | Power | Comfortable for | Price |
|---|---|---|---|---|
| Intel Arc B580 Best value | 12GB | 150W | 7B to 8B models, with some 13B experimentation. | Check price on Amazon |
| Mac Studio M3 Ultra (512GB) | 512GB | 250W | Huge FP4 and INT4 planning envelopes, including models that would otherwise force multi-GPU Linux boxes. | Check price on Amazon |
| Mac Studio M3 Ultra (256GB) | 256GB | 230W | Large expert and MoE models at aggressive quantization, especially when you want a single compact system. | Check price on Amazon |
| Mac Studio M4 Max (128GB) | 128GB | 170W | 70B-class planning with far better efficiency than large discrete-GPU towers. | Check price on Amazon |
| Mac mini M4 Pro (64GB) | 64GB | 80W | 7B to 32B local models when you value efficiency, silence, and unified memory over peak throughput. | Check price on Amazon |
| AMD RX 7900 XTX | 24GB | 355W | 30B-class planning when the software stack cooperates. | Check price on Amazon |
| Radeon Pro W7900 | 48GB | 295W | 70B-class models with more room for longer contexts and lighter batching. | Check price on Amazon |
| RTX 3090 (used) | 24GB | 350W | 30B to 34B models, with 70B planning at aggressive quantization. | Check price on Amazon |
| RTX A6000 (used) | 48GB | 300W | 70B-class local models and more serious context-heavy inference planning. | Check price on Amazon |
| RTX PRO 6000 Blackwell | 96GB | 600W | 120B-class single-GPU planning and much more breathing room for long contexts. | Check price on Amazon |
| 2x RTX PRO 6000 Blackwell | 192GB | 1200W | Large 70B to 120B-class planning with room for heavier contexts and batching. | Check price on Amazon |
| 4x RTX PRO 6000 Blackwell | 384GB | 2400W | Deep frontier-model planning where single-card and dual-card systems are no longer enough. | Check price on Amazon |
| 8x RTX PRO 6000 Blackwell | 768GB | 4800W | DeepSeek V3 and similar ~685B families when they stay on FP8-style deployment tiers. | Check price on Amazon |
| RTX 5090 | 32GB | 575W | 30B to 70B planning with the most room for context and quality. | Check price on Amazon |
| RTX 6000 Ada | 48GB | 300W | 70B-class deployment with more headroom for heavier precision and longer sessions. | Check price on Amazon |
| RTX 4090 | 24GB | 450W | 30B to 34B models, with 70B work possible but constrained. | Check price on Amazon |
| 8x NVIDIA H200 SXM | 1128GB | 5600W | 1T-class models at 8-bit style planning levels, plus more room for contexts and runtime headroom. | Check price on Amazon |
| 8x NVIDIA B200 | 1440GB | 8000W | The escape hatch for trillion-parameter local plans that still exceed H200-class memory budgets. | Check price on Amazon |
Cards are ranked by memory capacity per dollar using the 2026-07-11 planning-price snapshot. This is not a live-price feed and it is not the same as speed: tokens-per-second depends on measured throughput, which the benchmark dataset covers as it grows. Power is board TDP, a planning ceiling for running cost.
"Check price on Amazon" links are affiliate links. We may earn a commission at no extra cost to you. We never show a stored price — the live price is always the one on Amazon.
What you enter
- Model size target
- New or used condition
- Maximum budget
What it returns
- GPUs ranked by memory per dollar
- Best-value match
- VRAM and power per card
- What each card is comfortable running
- A live price check on Amazon
Unique value
This page combines a model-size floor, budget, condition, VRAM, power, and local-AI fit in one ranking. It is not a live-price feed: the internal price snapshot must be revalidated, and the current purchase price remains on the retailer listing.