Best local AI model for RTX 5080 16GB.

The RTX 5080 has far more compute than a 4060 Ti but the same 16GB ceiling. Use the extra speed for latency, batching, or image pipelines, not for forcing a 32B into too little memory. A used 24GB 3090 is a better LLM card if model size matters more than throughput.

Recommendation Full matrix

01 / Recommendation

Run this size class.

Recommended default

Qwen3-14B

Use Q4/Q5 GGUF or EXL2. This is the highest-scoring current open-weight model that fits this card cleanly, selected by benchmark then fit then freshness, not by parameter count.

Benchmark anchor

Same model ceiling as the 4060 Ti: Qwen3-14B. More compute does not create VRAM.

Evidence

There is no benchmark evidence that a 16GB GPU should default to 70B; the strongest fitting model is the 14B-class pick.

02 / Alternates

Other realistic picks.

Qwen3-14B at lower context for headroom

Qwen3-8B fast service

SDXL and Flux image pipelines

03 / More GPUs

Compare another card.

RTX 3060 12GB RTX 4060 Ti 16GB RTX 3090 24GB RTX 4090 24GB RTX 5090 32GB