Meta Llama 3.1 8B Instruct (FP32) — 23.4 GBon OVH l4-1-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 FP32 quantization (full-precision quality tier), the model weighs 29.92 GB. This exceeds the 24 GB of VRAM on OVH l4-1-gpu. Inference is still possible via CPU offload or memory-mapped loading from disk, but expect significantly reduced performance.
The NVIDIA L4 is a datacenter inference GPU with 24 GB of GDDR6 VRAM and 300 GB/s memory bandwidth. It delivers 121 FP16 TFLOPS with Ada Lovelace architecture. Designed for efficient, low-power inference workloads in cloud and edge deployments. Handles quantized models up to 20B parameters.
Hardware Requirements
| Model size | 29.92 GB |
| VRAM available | 24 GB |
| VRAM used | 23.4 GB |
| System RAM | 80 GB |
| Min RAM required | 8.4 GB |
| GPU layers | 23 / 32 |
| Context size | 5,327 |
| Backend | cuda13 |
| Flash attention | Yes |
| Reading from disk | Yes |
Performance Notes
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/fp32/nvidia-l4.yaml) applyGenerated values.yaml
/values/meta-llama-3-1-8b-instruct/fp32/nvidia-l4.yaml
Loading values…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. 23 of 32 layers fit in the 24 GB of VRAM on OVH l4-1-gpu; remaining layers are offloaded to CPU.
Can I run Meta Llama 3.1 8B Instruct on OVH l4-1-gpu?
Yes, with reduced performance. OVH l4-1-gpu can run Meta Llama 3.1 8B Instruct (FP32), but only 23 of 32 layers fit in VRAM. The rest are offloaded to CPU.
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 OVH l4-1-gpu, flash attention is enabled to maximise context length and throughput within the available 24 GB of VRAM.
Why are some layers offloaded to CPU?
OVH l4-1-gpu has 24 GB of VRAM, but Meta Llama 3.1 8B Instruct (FP32) requires approximately 29.92 GB. Only 23 of 32 layers fit in VRAM; the remaining layers run on CPU, which is slower but still functional.
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.