GPUs/NVIDIA A100 80GB/Llama 3.1 70B

Can NVIDIA A100 80GB run Llama 3.1 70B?

70B parameter LLM model on 80GB HBM2e

Yes — runs at 8-bit quantization
~18-23 tok/sUsable
SpeedFast inference, near-native speed
QualityNear-lossless — virtually identical to FP16

VRAM Requirements

Llama 3.1 70B is a 70B parameter model. At full precision (FP16), it requires 140GB of VRAM. Your NVIDIA A100 80GB has 80GB, so you'll need to quantize it to 8-bit (Q8) to fit.

FP16 (Full Precision)140GB (need 60GB more)

Maximum quality, no quantization

Q8 (8-bit)70GB (10GB free)

Near-lossless, ~50% size reduction

Q4 (4-bit)40GB (40GB 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

Running Llama 3.1 70B at 8-bit quantization on NVIDIA A100 80GB gives you virtually identical quality to full precision while using roughly half the VRAM. Most users cannot distinguish Q8 output from FP16. This is the recommended precision for daily use — it's the best balance of quality and resource usage.

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 70B

ollama run llama3.1:70b

This downloads the model (~70GB). 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 ~70GB 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 70B

Frontier-class open LLM. Q4 fits on dual 24GB GPUs or a single 48GB card.

Category: LLM · Parameters: 70B · CUDA required: No (runs via llama.cpp/GGUF)