A Complete Guide to Local AI Model Recommendations by Graphics Card

Which models can I run on my graphics card? Here is the answer to that question.

1. The Core Principle: VRAM Is the Model Ceiling

Runnable model = GPU VRAM ≥ model file size + KV cache headroom

If VRAM is smaller than the model size, it simply cannot run. No matter how fast the memory bandwidth is, there is no way around it.

2. NVIDIA GPU Spec Comparison

RTX 30 Series (Ampere)

RTX 40 Series (Ada Lovelace)

RTX 50 Series (Blackwell) — With GDDR7

3. Recommended Models by GPU

🟢 RTX 3060 12GB (used ~150,000-200,000 KRW)

With 12GB VRAM, 8B models run fully.

In a word: "For 150,000-200,000 KRW, 8B models comfortably, 14B tightly"

🔵 RTX 3090 24GB (used ~600,000-700,000 KRW) — 🏆 The best value

With 24GB VRAM, 32B models run fully and 70B partly.

In a word: "For 600,000-700,000 KRW, the king that fully runs 32B models"

🔷 RTX 4070 Ti Super 16GB (~1,150,000 KRW)

16GB + 672 GB/s is optimal for 14B models.

In a word: "For 1,150,000 KRW, the sweet spot for 14B models"

⭐ RTX 4060 Ti 16GB (~490,000 KRW)

16GB, but the bandwidth is low at 288 GB/s. It wins by capacity alone.

In a word: "For 490,000 KRW, 16GB — 14B is slow but it runs"

🏆 RTX 4090 24GB (~3,150,000 KRW)

The fastest consumer GPU plus 24GB VRAM.

In a word: "For 3,150,000 KRW, 8B at 135 TPS, 32B at 42 TPS — you never wait"

👑 RTX 5090 32GB (~7,400,000 KRW)

GDDR7's 1,792 GB/s bandwidth plus 32GB VRAM. The final word in local AI.

In a word: "For 7,400,000 KRW, running a 32B model at 142 TPS makes chat feel instant"

💰 RTX 5060 Ti 16GB (~930,000 KRW) — The new value king

GDDR7 at 448 GB/s. 55% faster bandwidth than the 4060 Ti 16GB.

In a word: "For 930,000 KRW, 8B at 51 TPS — 4070-class performance"

4. VRAM at a Glance

5. Value Rankings (2026)

Summary Formula

Budget under 200,000 KRW -> RTX 3060 12GB (used) + Llama 3.1 8B
Budget 600,000-700,000 KRW -> RTX 3090 24GB (used) + Qwen3 32B
Budget 900,000-1,200,000 KRW -> RTX 5060 Ti 16GB or RTX 4070 Ti Super 16GB
Budget 3,000,000 KRW+ -> RTX 4090 24GB + Qwen3 32B (42 TPS)
Budget 7,000,000 KRW+ -> RTX 5090 32GB + Qwen3 32B (142 TPS)

6. Frequently Asked Questions

Q. Can I run a 14B model on an 8GB GPU? → It runs at Q3_K_M quantization (~7.5GB), but the quality drops significantly. Running an 8B model at Q8_0 is better.

Q. 4060 Ti 16GB vs 3090 24GB, which is better? → The 3090 is 3x faster in bandwidth (936 vs 288 GB/s) and has 8GB more VRAM. At a similar budget when buying used at 600,000-700,000 KRW versus the 4060 Ti 16GB (490,000 KRW), the 3090 wins by a landslide.

Q. What about connecting two GPUs? → llama.cpp can split with --tensor-split, but the PCIe bus bottleneck means 1+1 ≠ 2. The speed is about 60-70%.

Q. What about AMD GPUs? → ROCm has a narrower support range than NVIDIA. It works via llama.cpp Vulkan, but performance drops 30-50% versus CUDA. NVIDIA is recommended.