Meta Llama 3.1 8B Instruct (FP32) — 47.2 GBon NVIDIA L40S 384GB

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 FP32 quantization (full-precision quality tier), the model weighs 29.92 GB. This fits within the 384 GB of VRAM on NVIDIA L40S 384GB, enabling full GPU inference.

Hardware Requirements

Model size	29.92 GB
VRAM available	384 GB
VRAM used	47.2 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/fp32/nvidia-l40s-384gb.yaml) apply

Generated `values.yaml`

/values/meta-llama-3-1-8b-instruct/fp32/nvidia-l40s-384gb.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-f32.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 (FP32) need?

The FP32 quantization of Meta Llama 3.1 8B Instruct requires 29.92 GB. All 32 layers fit in the 384 GB of VRAM available on NVIDIA L40S 384GB, enabling full GPU acceleration.

Can I run Meta Llama 3.1 8B Instruct on NVIDIA L40S 384GB?

Yes. NVIDIA L40S 384GB provides 384 GB of VRAM, which is enough to run Meta Llama 3.1 8B Instruct (FP32) 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. FP32 compresses Meta Llama 3.1 8B Instruct from its original size down to 29.92 GB.

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

FP32 is a full-precision 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 384GB, flash attention is enabled to maximise context length and throughput within the available 384 GB of VRAM.

How do I run Meta Llama 3.1 8B Instruct (FP32) with Ollama?

Run ollama run llama3.1:8b-instruct-fp32 to start Meta Llama 3.1 8B Instruct (FP32). Ollama handles downloading the model weights automatically on first run.

Last updated: March 5, 2026