Meta Llama 3.1 8B Instruct (Q3_K_XL) — 21.7 GBon NVIDIA H100
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 Q3_K_XL quantization (low quality tier), the model weighs 4.45 GB. This fits within the 80 GB of VRAM on NVIDIA H100, enabling full GPU inference.
The NVIDIA H100 80 GB is a datacenter GPU with 80 GB of HBM3 VRAM and 3350 GB/s memory bandwidth. It delivers 1979 FP16 TFLOPS on the Hopper architecture, making it the fastest single-GPU option for large language model inference. It handles models up to 70B parameters with high throughput. Built for datacenter teams running demanding production AI workloads.
Hardware Requirements
| Model size | 4.45 GB |
| VRAM available | 80 GB |
| VRAM used | 21.7 GB |
| System RAM | |
| 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/q3_k_xl/nvidia-h100-80gb.yaml) applyGenerated values.yaml
/values/meta-llama-3-1-8b-instruct/q3_k_xl/nvidia-h100-80gb.yaml
Loading values…Frequently Asked Questions
How much VRAM does Meta Llama 3.1 8B Instruct (Q3_K_XL) need?
The Q3_K_XL quantization of Meta Llama 3.1 8B Instruct requires 4.45 GB. All 32 layers fit in the 80 GB of VRAM available on NVIDIA H100, enabling full GPU acceleration.
Can I run Meta Llama 3.1 8B Instruct on NVIDIA H100?
Yes. NVIDIA H100 provides 80 GB of VRAM, which is enough to run Meta Llama 3.1 8B Instruct (Q3_K_XL) 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. Q3_K_XL compresses Meta Llama 3.1 8B Instruct from its original size down to 4.45 GB.
What quantization should I choose for Meta Llama 3.1 8B Instruct?
Q3_K_XL 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 NVIDIA H100, 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 (Q3_K_XL) with Ollama?
Run ollama run llama3.1:8b-instruct-q3_k_xl to start Meta Llama 3.1 8B Instruct (Q3_K_XL). Ollama handles downloading the model weights automatically on first run.