Llama 3.3 70B Instruct (FP16) — 23.4 GBon OVH l4-1-gpu

FP16 OVH l4-1-gpu

Overview

Llama 3.3 70B Instruct is a 70B parameter dense language model by Meta, with code, multilingual, tool-calls capabilities. It supports a context window of up to 131,072 tokens.

Llama 3.3 70B Instruct is a 70-billion-parameter dense transformer model from Meta, optimized for instruction following, code generation, and multilingual conversation. It delivers performance competitive with larger models in the Llama family while remaining practical for single-node GPU deployments. The model supports tool calling and eight languages including English, French, Spanish, and German. With a 128K context window and grouped-query attention, it quantizes efficiently down to Q4 levels for self-hosted inference on consumer hardware.

At FP16 quantization (full-precision quality tier), the model weighs 131.43 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	131.43 GB
VRAM available	24 GB
VRAM used	23.4 GB
System RAM	80 GB
Min RAM required	110.1 GB
GPU layers	13 / 80
Context size	2,614
Backend	cuda13
Flash attention	Yes
Reading from disk	Yes

Performance Notes

Reduced performance

67 of 80 layers will run on CPU, which may reduce inference speed.

Major performance impact

The model size exceeds available VRAM + RAM. Data will be read from disk on demand, which significantly slows inference.

Shorter conversations

Context size has been reduced due to memory constraints. This may affect the model's ability to handle long conversations.

Deploy

Server Local

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/llama-3-3-70b-instruct/fp16/nvidia-l4.yaml) apply

Generated values.yaml

/values/llama-3-3-70b-instruct/fp16/nvidia-l4.yaml

Loading values…

Install llama.cpp

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

Download Model

curl -L -o llama-3-3-70b-instruct.gguf "https://huggingface.co/bartowski/Llama-3.3-70B-Instruct-GGUF/resolve/main/Llama-3.3-70B-Instruct-f16/Llama-3.3-70B-Instruct-f16-00001-of-00004.gguf"

Start Server

llama-server \
  -m llama-3-3-70b-instruct.gguf \
  --n-gpu-layers 13 \
  --ctx-size 2614 \
  --flash-attn

Verify

curl http://localhost:8080/health

Explore

• Configure a deployment
• Browse models
• Browse hardware

Other quantizations

• Llama 3.3 70B Instruct (Q8_0)
• Llama 3.3 70B Instruct (Q5_K_M)
• Llama 3.3 70B Instruct (Q4_K_M)

Other hardware

• Llama 3.3 70B Instruct on CPU Only
• Llama 3.3 70B Instruct on NVIDIA A100 80GB
• Llama 3.3 70B Instruct on NVIDIA H100

Frequently Asked Questions

How much VRAM does Llama 3.3 70B Instruct (FP16) need?

The FP16 quantization of Llama 3.3 70B Instruct requires 131.43 GB. 13 of 80 layers fit in the 24 GB of VRAM on OVH l4-1-gpu; remaining layers are offloaded to CPU.

Can I run Llama 3.3 70B Instruct on OVH l4-1-gpu?

Yes, with reduced performance. OVH l4-1-gpu can run Llama 3.3 70B Instruct (FP16), but only 13 of 80 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. FP16 compresses Llama 3.3 70B Instruct from its original size down to 131.43 GB.

What quantization should I choose for Llama 3.3 70B Instruct?

FP16 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 Llama 3.3 70B 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 Llama 3.3 70B Instruct (FP16) requires approximately 131.43 GB. Only 13 of 80 layers fit in VRAM; the remaining layers run on CPU, which is slower but still functional.

How do I run Llama 3.3 70B Instruct (FP16) with Ollama?

Run ollama run llama3.3:70b-instruct-fp16 to start Llama 3.3 70B Instruct (FP16). Ollama handles downloading the model weights automatically on first run.

Last updated: March 5, 2026