GPUs/NVIDIA A100 80GB/Llama 3.1 8B

Can NVIDIA A100 80GB run Llama 3.1 8B?

8B parameter LLM model on 80GB HBM2e

Yes — runs at full precision
~76-94 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 A100 80GB has 80GB — enough to run it without any quantization.

FP16 (Full Precision)16GB (64GB free)

Maximum quality, no quantization

Q8 (8-bit)8GB (72GB free)

Near-lossless, ~50% size reduction

Q4 (4-bit)5GB (75GB free)

Good quality, ~75% size reduction

Your GPU VRAM: 80GB HBM2e at 2039 GB/s bandwidth
Recommended system RAM: 160GB DDR5 (2x GPU VRAM minimum for model overflow)

What This Means in Practice

With NVIDIA A100 80GB 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 A100 80GB Specs

VRAM80GB HBM2e
Memory Bandwidth2039 GB/s
TDP300W
CUDA Cores6,912
Street Price~$8000
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)