Can I run Llama-3.1-70B-Instruct on a NVIDIA H200?
Yes.Runs
You can run this with Q8 quantization. On a NVIDIA H200 (141 GB VRAM), Llama-3.1-70B-Instruct needs about 86 GB (Q8) and should generate at roughly ~43 tok/s. Recommended: Run on your GPU with Q8.
meta-llama/Llama-3.1-70B-InstructWhy
- Full precision needs ~170 GB which exceeds your accelerator, but 8-bit (INT8 / Q8) needs only ~86 GB and fits.
How to run Llama-3.1-70B-Instruct on a H200
Run with Ollama (easiest)
ollama run llama3.1:70bOr with llama.cpp
llama-cli -hf meta-llama/Llama-3.1-70B-Instruct -p "Hello"Serve with vLLM (OpenAI-compatible API)
# Fast production serving on http://localhost:8000/v1
vllm serve meta-llama/Llama-3.1-70B-InstructPython (Transformers)
from transformers import pipeline, BitsAndBytesConfig
# pip install transformers accelerate bitsandbytes
quant = BitsAndBytesConfig(load_in_8bit=True)
pipe = pipeline("text-generation", model="meta-llama/Llama-3.1-70B-Instruct", device_map="auto",
model_kwargs={"quantization_config": quant})
print(pipe("Hello", max_new_tokens=50))