NVIDIA GeForce RTX 3080 10GB
LLM Inference Performance
| Model | Tokens / sec | Local Fit |
|---|---|---|
| Mistral 7b Q4 | 51 tok/s | fits · single GPU |
| Llama 3 8b Q4 | 47 tok/s | fits · single GPU |
| Llama 3 13b Q4 | — OOM — | OOM / offload |
| Llama 3 70b Q4 | — OOM — | OOM / offload |
Local Model Compatibility
Spec Sheet
Analysis notes
Quick Summary
NVIDIA GeForce RTX 3080 10GB is a 10GB NVIDIA card for local AI workloads. It uses Ampere, draws about 320W, and is mostly a 7B-class local LLM card. For AI buyers, the main questions are VRAM ceiling, CUDA support, memory bandwidth, and used-market price.
Specs That Matter for AI
The 10GB 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 760 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 10GB starts as a 3.3/5 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 10GB run local LLMs?
- Yes. With 10GB of VRAM, it can run 7B quantized models locally, but 13B models are tight.
- Is the NVIDIA GeForce RTX 3080 10GB good for AI inference?
- It benefits from CUDA support, which is the safest compatibility path for most AI tools.