Meta Llama 3.1 8B Instruct (Q2_K_L) — 20.7 GBon NVIDIA RTX 5090

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 Q2_K_L quantization (low quality tier), the model weighs 3.44 GB. This fits within the 32 GB of VRAM on NVIDIA RTX 5090, enabling full GPU inference.

Hardware Requirements

Model size	3.44 GB
VRAM available	32 GB
VRAM used	20.7 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_toolkit

Command

helmfile --state-values-file <(curl -s https://www.prositronic.eu/values/meta-llama-3-1-8b-instruct/q2_k_l/nvidia-rtx5090.yaml) apply

Generated `values.yaml`

/values/meta-llama-3-1-8b-instruct/q2_k_l/nvidia-rtx5090.yaml

Loading values…

Install llama.cpp

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

Download Model

curl -L -o meta-llama-3-1-8b-instruct.gguf "https://huggingface.co/bartowski/Meta-Llama-3.1-8B-Instruct-GGUF/resolve/main/Meta-Llama-3.1-8B-Instruct-Q2_K_L.gguf"

Start Server

llama-server \
  -m meta-llama-3-1-8b-instruct.gguf \
  --n-gpu-layers 32 \
  --ctx-size 131072 \
  --flash-attn

Verify

curl http://localhost:8080/health

Frequently Asked Questions

How much VRAM does Meta Llama 3.1 8B Instruct (Q2_K_L) need?

The Q2_K_L quantization of Meta Llama 3.1 8B Instruct requires 3.44 GB. All 32 layers fit in the 32 GB of VRAM available on NVIDIA RTX 5090, enabling full GPU acceleration.

Can I run Meta Llama 3.1 8B Instruct on NVIDIA RTX 5090?

Yes. NVIDIA RTX 5090 provides 32 GB of VRAM, which is enough to run Meta Llama 3.1 8B Instruct (Q2_K_L) 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. Q2_K_L compresses Meta Llama 3.1 8B Instruct from its original size down to 3.44 GB.

What quantization should I choose for Meta Llama 3.1 8B Instruct?

Q2_K_L 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 RTX 5090, flash attention is enabled to maximise context length and throughput within the available 32 GB of VRAM.

How do I run Meta Llama 3.1 8B Instruct (Q2_K_L) with Ollama?

Run ollama run llama3.1:8b-instruct-q2_k_l to start Meta Llama 3.1 8B Instruct (Q2_K_L). Ollama handles downloading the model weights automatically on first run.

Last updated: March 5, 2026