Can NVIDIA RTX 5000 Ada run Llama 3.1 8B?

8B parameter LLM model on 32GB GDDR6 ECC

Yes — runs at full precision
~20-25 tok/sUsable
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 RTX 5000 Ada 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 GDDR6 ECC at 576 GB/s bandwidth
Recommended system RAM: 64GB DDR5 (2x GPU VRAM minimum for model overflow)

What This Means in Practice

With NVIDIA RTX 5000 Ada 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 RTX 5000 Ada Specs

VRAM32GB GDDR6 ECC
Memory Bandwidth576 GB/s
TDP250W
CUDA Cores12,800
Street Price~$3800
AI Rating9/10

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)