Can AMD Radeon PRO W7900 run Llama 3.1 70B?

70B parameter LLM model on 48GB GDDR6 ECC

Yes — runs at 4-bit quantization
~9-11 tok/sSlow
SpeedModerate speed, usable for interactive chat
QualityGood quality with slight degradation on complex reasoning
AMD GPUs lack CUDA. While Llama 3.1 70B can technically run via llama.cpp/GGUF, the setup is more complex and less optimized than on NVIDIA hardware.

VRAM Requirements

Llama 3.1 70B is a 70B parameter model. At full precision (FP16), it requires 140GB of VRAM. Your AMD Radeon PRO W7900 has 48GB, so you'll need to quantize it to 4-bit (Q4) to fit.

FP16 (Full Precision)140GB (need 92GB more)

Maximum quality, no quantization

Q8 (8-bit)70GB (need 22GB more)

Near-lossless, ~50% size reduction

Q4 (4-bit)40GB (8GB free)

Good quality, ~75% size reduction

Your GPU VRAM: 48GB GDDR6 ECC at 864 GB/s bandwidth
Recommended system RAM: 96GB DDR5 (2x GPU VRAM minimum for model overflow)

What This Means in Practice

At 4-bit quantization, Llama 3.1 70B fits in AMD Radeon PRO W7900's 48GB VRAM but with some quality trade-offs. Complex reasoning tasks and nuanced writing may show slight degradation. For casual chat, code assistance, and general queries, Q4 is perfectly usable. For critical work, consider a GPU with more VRAM to run at Q8.

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:q4_K_M

This downloads the Q4_K_M quantized version (~40GB). First run takes a few minutes to download.

Step 3: Verify GPU is being used

rocm-smi

Check that VRAM usage increases when the model loads. You should see ~40GB used.

AMD Radeon PRO W7900 Specs

VRAM48GB GDDR6 ECC
Memory Bandwidth864 GB/s
TDP295W
CUDA CoresN/A
Street Price~$3600
AI Rating6/10

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)