NVIDIA GeForce RTX 5080
LLM Inference Performance
| Model | Tokens / sec | Local Fit |
|---|---|---|
| Mistral 7b Q4 | 96 tok/s | fits · single GPU |
| Llama 3 8b Q4 | 89 tok/s | fits · single GPU |
| Llama 3 13b Q4 | 50 tok/s | fits · single GPU |
| Llama 3 70b Q4 | — OOM — | OOM / offload |
Local Model Compatibility
Spec Sheet
Analysis notes
Quick Summary
NVIDIA GeForce RTX 5080 is a 16GB NVIDIA card for local AI workloads. It uses Blackwell, draws about 360W, 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 16GB VRAM pool sets the practical model-size limit. Sixteen gigabytes or more gives room for 7B models, many 13B quantized models, and heavier image-generation workflows. Memory bandwidth is listed at roughly 960 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 5080 starts as a 3.8/5 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 5080 run local LLMs?
- Yes. With 16GB of VRAM, it can run 7B quantized models locally and many 13B quantized models with practical settings.
- Is the NVIDIA GeForce RTX 5080 good for AI inference?
- It benefits from CUDA support, which is the safest compatibility path for most AI tools.