Llama 3.3 70B Instruct (Q4_K_S) — 78.9 GBon NVIDIA H100 160GB

Overview

Llama 3.3 70B Instruct is a 70B parameter dense language model by Meta, with code, multilingual, tool-calls capabilities. It supports a context window of up to 131,072 tokens.

Llama 3.3 70B Instruct is a 70-billion-parameter dense transformer model from Meta, optimized for instruction following, code generation, and multilingual conversation. It delivers performance competitive with larger models in the Llama family while remaining practical for single-node GPU deployments. The model supports tool calling and eight languages including English, French, Spanish, and German. With a 128K context window and grouped-query attention, it quantizes efficiently down to Q4 levels for self-hosted inference on consumer hardware.

At Q4_K_S quantization (medium quality tier), the model weighs 37.58 GB. This fits within the 160 GB of VRAM on NVIDIA H100 160GB, enabling full GPU inference.

Hardware Requirements

Model size	37.58 GB
VRAM available	160 GB
VRAM used	78.9 GB
System RAM
Min RAM required	0 GB
GPU layers	80 / 80
Context size	131,072
Backend	cuda13
Flash attention	Yes
Reading from disk	Yes

Deploy

Prerequisites

Ensure your GPU nodes are prepared with the NVIDIA container toolkit:

ansible-playbook prositronic.infra.nvidia_container_toolkit

Command

helmfile --state-values-file <(curl -s https://www.prositronic.eu/values/llama-3-3-70b-instruct/q4_k_s/nvidia-h100-160gb.yaml) apply

Generated `values.yaml`

/values/llama-3-3-70b-instruct/q4_k_s/nvidia-h100-160gb.yaml

Loading values…

Install llama.cpp

Install llama.cpp from the official build instructions for your platform and backend.

Download Model

curl -L -o llama-3-3-70b-instruct.gguf "https://huggingface.co/bartowski/Llama-3.3-70B-Instruct-GGUF/resolve/main/Llama-3.3-70B-Instruct-Q4_K_S.gguf"

Start Server

llama-server \
  -m llama-3-3-70b-instruct.gguf \
  --n-gpu-layers 80 \
  --ctx-size 131072 \
  --flash-attn

Verify

curl http://localhost:8080/health

Frequently Asked Questions

How much VRAM does Llama 3.3 70B Instruct (Q4_K_S) need?

The Q4_K_S quantization of Llama 3.3 70B Instruct requires 37.58 GB. All 80 layers fit in the 160 GB of VRAM available on NVIDIA H100 160GB, enabling full GPU acceleration.

Can I run Llama 3.3 70B Instruct on NVIDIA H100 160GB?

Yes. NVIDIA H100 160GB provides 160 GB of VRAM, which is enough to run Llama 3.3 70B Instruct (Q4_K_S) with all layers on the GPU for optimal performance.

What is quantization?

Quantization reduces a model's numerical precision from its original floating-point format to a more compact representation. This shrinks the file size and VRAM footprint, making it possible to run large models on consumer hardware. The trade-off is a small reduction in output quality. Q4_K_S compresses Llama 3.3 70B Instruct from its original size down to 37.58 GB.

What quantization should I choose for Llama 3.3 70B Instruct?

Q4_K_S is a medium-quality quantization. Higher-quality quants (Q8, Q6) preserve more model accuracy but need more VRAM. Lower quants (Q4, Q3, Q2) reduce VRAM usage at the cost of some quality. Choose based on your available hardware and quality requirements.

What is flash attention and why is it enabled?

Flash attention is a memory-efficient algorithm that speeds up the attention mechanism in transformer models. It reduces VRAM usage during inference by avoiding the materialisation of the full attention matrix. For Llama 3.3 70B Instruct on NVIDIA H100 160GB, flash attention is enabled to maximise context length and throughput within the available 160 GB of VRAM.

How do I run Llama 3.3 70B Instruct (Q4_K_S) with Ollama?

Run ollama run llama3.3:70b-instruct-q4_k_s to start Llama 3.3 70B Instruct (Q4_K_S). Ollama handles downloading the model weights automatically on first run.

Last updated: March 5, 2026