Can NVIDIA GeForce RTX 5090 run Llama 3.1 8B?

8B parameter LLM model on 32GB GDDR7

Yes — runs at full precision
~65-80 tok/sExcellent
SpeedFastest possible inference
QualityMaximum quality, no degradation

VRAM Requirements

Llama 3.1 8B is a 8B parameter model. At full precision (FP16), it requires 16GB of VRAM. Your NVIDIA GeForce RTX 5090 has 32GB — enough to run it without any quantization.

FP16 (Full Precision)16GB (16GB free)

Maximum quality, no quantization

Q8 (8-bit)8GB (24GB free)

Near-lossless, ~50% size reduction

Q4 (4-bit)5GB (27GB free)

Good quality, ~75% size reduction

Your GPU VRAM: 32GB GDDR7 at 1792 GB/s bandwidth
Recommended system RAM: 64GB DDR5 (2x GPU VRAM minimum for model overflow)

What This Means in Practice

With NVIDIA GeForce RTX 5090 running Llama 3.1 8B at full precision, you get the highest quality responses with no quantization artifacts. This is ideal for tasks requiring nuanced reasoning, creative writing, and complex analysis. You'll have the best possible experience with this model.

How to Set It Up

Step 1: Install Ollama

curl -fsSL https://ollama.com/install.sh | sh

Ollama is the easiest way to run local LLMs. Works on Linux, macOS, and Windows.

Step 2: Download and run Llama 3.1 8B

ollama run llama3.1:8b

This downloads the model (~16GB). First run takes a few minutes.

Step 3: Verify GPU is being used

nvidia-smi

Check that VRAM usage increases when the model loads. You should see ~16GB used.

NVIDIA GeForce RTX 5090 Specs

VRAM32GB GDDR7
Memory Bandwidth1792 GB/s
TDP575W
CUDA Cores21,760
Street Price~$2800
AI Rating10/10

About Llama 3.1 8B

Great entry point. Runs well on 8GB+ GPUs at Q4.

Category: LLM · Parameters: 8B · CUDA required: No (runs via llama.cpp/GGUF)