Meta Llama 3.1 8B Instruct (Q4_0_4_8) — 21.6 GBon OVH a100-1-gpu
Overview
Meta Llama 3.1 8B Instruct is a 8B parameter dense language model by Meta, with code, multilingual, tool-calls capabilities. It supports a context window of up to 131,072 tokens.
Meta Llama 3.1 8B Instruct is an 8-billion-parameter dense transformer model from Meta, designed for instruction following, code generation, and multilingual tasks. It offers a strong balance of quality and efficiency in the small-model category, outperforming many 7B-class alternatives on standard benchmarks. The model supports tool calling and eight languages including English, German, and French. With a 128K context window and flash attention support, it runs comfortably on a single consumer GPU at Q4 quantization levels.
At Q4_0_4_8 quantization (low quality tier), the model weighs 4.34 GB. This fits within the 80 GB of VRAM on OVH a100-1-gpu, enabling full GPU inference.
The NVIDIA A100 80 GB is a datacenter GPU with 80 GB of HBM2e VRAM and 2039 GB/s memory bandwidth. It delivers 312 FP16 TFLOPS, enabling fast inference on large language models up to 70B parameters at moderate quantization. Well suited for datacenter teams running production LLM workloads that require high memory capacity and throughput.
Hardware Requirements
| Model size | 4.34 GB |
| VRAM available | 80 GB |
| VRAM used | 21.6 GB |
| System RAM | 160 GB |
| Min RAM required | 0 GB |
| GPU layers | 32 / 32 |
| 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_toolkitCommand
helmfile --state-values-file <(curl -s https://www.prositronic.eu/values/meta-llama-3-1-8b-instruct/q4_0_4_8/nvidia-a100-80gb.yaml) applyGenerated values.yaml
/values/meta-llama-3-1-8b-instruct/q4_0_4_8/nvidia-a100-80gb.yaml
Loading values…Frequently Asked Questions
How much VRAM does Meta Llama 3.1 8B Instruct (Q4_0_4_8) need?
The Q4_0_4_8 quantization of Meta Llama 3.1 8B Instruct requires 4.34 GB. All 32 layers fit in the 80 GB of VRAM available on OVH a100-1-gpu, enabling full GPU acceleration.
Can I run Meta Llama 3.1 8B Instruct on OVH a100-1-gpu?
Yes. OVH a100-1-gpu provides 80 GB of VRAM, which is enough to run Meta Llama 3.1 8B Instruct (Q4_0_4_8) 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_0_4_8 compresses Meta Llama 3.1 8B Instruct from its original size down to 4.34 GB.
What quantization should I choose for Meta Llama 3.1 8B Instruct?
Q4_0_4_8 is a low-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 Meta Llama 3.1 8B Instruct on OVH a100-1-gpu, flash attention is enabled to maximise context length and throughput within the available 80 GB of VRAM.
How do I run Meta Llama 3.1 8B Instruct (Q4_0_4_8) with Ollama?
Run ollama run llama3.1:8b-instruct-q4_0_4_8 to start Meta Llama 3.1 8B Instruct (Q4_0_4_8). Ollama handles downloading the model weights automatically on first run.