Best Budget GPUs for Local LLMs in 2026: 16GB Cards, Used RTX 3090 & Current Prices

For local LLMs, the most important thing about a graphics card is how much VRAM it has, followed by its memory bandwidth. Raw gaming performance matters much less. That’s why budget AI builders have long favoured 16GB cards and the used 24GB RTX 3090. In 2026 there’s a complication: a global memory shortage has pushed GPU prices far above launch prices, especially for cards with lots of VRAM. This guide explains which cards make sense for local AI now and what they can realistically run. If you have settled on 16GB, the bandwidth spread within that tier is the next thing to check.

This guide focuses on value. If you want the wider picture — what each VRAM tier unlocks, and how capacity and bandwidth divide the job between them — start with our guide to the best GPU for AI in 2026.

Key Takeaways

• 16GB is the practical minimum for a GPU-based local AI setup in 2026. 12GB works for smaller models only.
• A used RTX 3090 (24GB) remains the value pick for running 27–32B models entirely on one card, if you can find a healthy one at a fair price (~$1,000 on eBay in September 2026).
• The RTX 5060 Ti 16GB is the most accessible new NVIDIA 16GB card, but it currently sells for about $680–730, well above its $429 launch price.
• Before buying a GPU, compare with unified-memory machines. A Mac mini or AMD Strix Halo mini PC can run larger models than a 16GB card, although more slowly.

Budget GPU Comparison for Local AI

Launch prices are manufacturer MSRPs. 2026 street prices vary widely, so check current listings.

What Each Card Can Run

• 12GB (Arc B580): 8–12B models at 4-bit with room for context, such as Qwen3 8B and Gemma 4 12B.
• 16GB (RTX 5060 Ti, 5070 Ti, RX 9060 XT): up to ~14B models comfortably, plus gpt-oss-20b, which is designed to fit in about 16GB. MoE models such as Qwen3.6-35B-A3B only fit if some weights are offloaded to system RAM, which slows them down.
• 24GB (used RTX 3090): 27–32B dense models at 4-bit, such as Qwen3.8-27B and Gemma 4 31B, which are strong coding and reasoning models.

Estimated Speeds

Using the rule of thumb tokens/sec ≈ bandwidth × 0.6–0.75 ÷ model size in GB, for an 8B model at 4-bit (~5GB):

• RTX 4060 Ti 16GB (288GB/s): ~35–43 tok/s
• RX 9060 XT 16GB (322GB/s): ~39–48 tok/s
• RTX 5060 Ti 16GB (448GB/s): ~54–67 tok/s
• RTX 5070 Ti (896GB/s): ~108–134 tok/s
• RTX 3090 (936GB/s): ~112–140 tok/s

For a 27B model at 4-bit (~17GB) on a 3090, expect roughly 33–41 tok/s. All figures are estimates from specs, not measurements. Actual speed depends on drivers and runtime. Compare more cards in the Local Hardware Matrix or head-to-head with our comparison tool.

Buying Advice for 2026

• Stick to 16GB+ for new purchases. 8GB cards are too limiting for local AI.
• Buying used? Ask for proof of stable operation, avoid cards with heavy mining or wear, and make sure your power supply can handle a 3090.
• Calculate before you spend. Use the VRAM Calculator to check that the models you care about fit, and the API Pricing Break-Even Calculator to see whether cloud APIs would be cheaper for your usage.
• Consider unified memory. With GPU prices inflated, a 64GB Mac mini M5 Pro or a 128GB Strix Halo system can be better value for large models. See our Strix Halo vs Apple guide and best Mac for local LLMs.

FAQ

Is 12GB of VRAM enough for local AI?

For 8–12B models, yes. For 27B+ models you’ll need 24GB, or you’ll have to offload to system RAM, which is much slower.

Why is the used RTX 3090 still recommended?

It’s the most affordable way to get 24GB of fast VRAM on a single, well-supported CUDA card.

Sources

NVIDIA, AMD and Intel specifications. 2026 pricing from TechSpot and TechPowerUp. Used RTX 3090 pricing from BestValueGPU.

Updated September 11, 2026: updated for 2026 GPU prices and current models, with estimated speeds replacing earlier benchmark claims.