Meta Llama 3.1 8B Instruct (Q4_0_4_4) — 21.6 GBon NVIDIA L40S
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_4 quantization (low quality tier), the model weighs 4.34 GB. This fits within the 48 GB of VRAM on NVIDIA L40S, enabling full GPU inference.
The NVIDIA L40S is a datacenter GPU with 48 GB of GDDR6 VRAM and 864 GB/s memory bandwidth. It delivers 362 FP16 TFLOPS with Ada Lovelace architecture. A versatile GPU for AI inference, training, and graphics workloads. Handles quantized models up to 40B parameters comfortably.
Hardware Requirements
| Model size | 4.34 GB |
| VRAM available | 48 GB |
| VRAM used | 21.6 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/q4_0_4_4/nvidia-l40s.yaml) applyGenerated values.yaml
/values/meta-llama-3-1-8b-instruct/q4_0_4_4/nvidia-l40s.yaml
Loading values…Frequently Asked Questions
How much VRAM does Meta Llama 3.1 8B Instruct (Q4_0_4_4) need?
The Q4_0_4_4 quantization of Meta Llama 3.1 8B Instruct requires 4.34 GB. All 32 layers fit in the 48 GB of VRAM available on NVIDIA L40S, enabling full GPU acceleration.
Can I run Meta Llama 3.1 8B Instruct on NVIDIA L40S?
Yes. NVIDIA L40S provides 48 GB of VRAM, which is enough to run Meta Llama 3.1 8B Instruct (Q4_0_4_4) 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_4 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_4 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 L40S, flash attention is enabled to maximise context length and throughput within the available 48 GB of VRAM.
How do I run Meta Llama 3.1 8B Instruct (Q4_0_4_4) with Ollama?
Run ollama run llama3.1:8b-instruct-q4_0_4_4 to start Meta Llama 3.1 8B Instruct (Q4_0_4_4). Ollama handles downloading the model weights automatically on first run.