NVIDIA GeForce RTX 3080 Ti
LLM Inference Performance
| Model | Tokens / sec | Local Fit |
|---|---|---|
| Mistral 7b Q4 | 58 tok/s | fits · single GPU |
| Llama 3 8b Q4 | 54 tok/s | fits · single GPU |
| Llama 3 13b Q4 | 30 tok/s | fits · single GPU |
| Llama 3 70b Q4 | — OOM — | OOM / offload |
Local Model Compatibility
Spec Sheet
Analysis notes
Quick Summary
NVIDIA GeForce RTX 3080 Ti is a 12GB NVIDIA card for local AI workloads. It uses Ampere, draws about 350W, and can run many 13B quantized models locally. For AI buyers, the main questions are VRAM ceiling, CUDA support, memory bandwidth, and used-market price.
Specs That Matter for AI
The 12GB VRAM pool sets the practical model-size limit. Below 12GB, local LLM use becomes tighter and often requires smaller quantizations, smaller context windows, or CPU offload. Memory bandwidth is listed at roughly 912 GB/s, which helps token generation when the whole model fits on card.
AI Workload Fit
CUDA is the platform note to verify first. CUDA keeps this card broadly compatible with PyTorch, vLLM, TensorRT-LLM, Ollama, llama.cpp CUDA builds, and most Stable Diffusion tooling. The card does not have enough VRAM for comfortable 70B 4-bit inference.
Verdict
NVIDIA GeForce RTX 3080 Ti starts as a 3.5/5 AI-value candidate in this seed catalog. That rating should be refined after Playwright harvest pulls rendered review pages, benchmark tables, and firsthand reports into the evidence corpus.
Frequently Asked Questions
- Can the NVIDIA GeForce RTX 3080 Ti run local LLMs?
- Yes. With 12GB of VRAM, it can run 7B quantized models locally and many 13B quantized models with practical settings.
- Is the NVIDIA GeForce RTX 3080 Ti good for AI inference?
- It benefits from CUDA support, which is the safest compatibility path for most AI tools.