NVIDIA GeForce RTX 3080 Ti — Local LLM Performance & Compatibility

Written by Jakub Rusinowski · Last updated September 19, 2026

12 GB at 912 GB/s — unusually high bandwidth for the capacity, which suits models that fit in 12 GB and are bandwidth-bound. Used-market only.

Technical Specifications

VRAM12 GB
Memory Bandwidth912 GB/s
TDP350 W
ArchitectureAmpere GA102
Release Year2021
MSRP at Launch$1,199
Inference Speed (Llama 3.1 8B Q4_K_M)59–112 tok/s (estimated)
Inference Speed (Llama 3.3 70B Q4_K_M)Does not fit — needs ~44 GB of 12 GB usable
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LLMs Compatible with 12 GB VRAM

All models below run comfortably in 12 GB VRAM with Q4_K_M quantization.

StarCoder 2StarCoder 2 15B · 10 GB VRAM · Q4_K_M · ollama run starcoder2:15b
Qwen 3Qwen 3 14B · 10 GB VRAM · Q4_K_M · ollama run qwen3:14b
DeepSeek R1DeepSeek R1 Distill Qwen 14B · 9 GB VRAM · Q4_K_M · ollama run deepseek-r1:14b
Phi-4 FamilyPhi-4 (14B) · 9 GB VRAM · Q4_K_M · ollama run phi4
Qwen 2.5 FamilyQwen 2.5 14B Instruct · 9 GB VRAM · Q4_K_M · ollama run qwen2.5:14b
Cogito v1Cogito v1 14B · 9 GB VRAM · Q4_K_M · ollama run cogito:14b
Ministral 3Ministral 3 14B · 9 GB VRAM · Q4_K_M · ollama run ministral-3:14b
OLMo 2OLMo 2 13B Instruct · 9 GB VRAM · Q4_K_M · ollama run olmo2:13b

36 more families also fit 12 GB — browse the full model library.

Best Use Cases

FAQ

Can the NVIDIA GeForce RTX 3080 Ti run local LLMs?

Yes — the NVIDIA GeForce RTX 3080 Ti has 12 GB VRAM and runs 12 GB at 912 GB/s — unusually high bandwidth for the capacity, which suits models that fit in 12 GB and are bandwidth-bo

How fast is the NVIDIA GeForce RTX 3080 Ti for AI inference?

The NVIDIA GeForce RTX 3080 Ti is estimated to run Llama 3.1 8B at 59–112 tok/s with Q4_K_M quantization. Llama 3.3 70B does not fit: it needs about 44 GB against 12 GB usable. These are modelled estimates, not measurements — see /en/methodology.

What LLMs can I run on 12 GB VRAM?

With 12 GB you can run: StarCoder 2, Qwen 3, DeepSeek R1, Phi-4 Family, Qwen 2.5 Family. Use Ollama for the easiest setup: ollama run llama3.1:8b.

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