Llama 4 Scout 17B 16E Instruct (Q4_1) — 385.3 GBon OVH h100-1-gpu

Q4_1 OVH h100-1-gpu

Overview

Llama 4 Scout 17B 16E Instruct is a 17B parameter moe language model by Meta, with code, multilingual, tool-calls, vision capabilities. It supports a context window of up to 10,485,760 tokens.

Llama 4 Scout 17B 16E Instruct is a Mixture-of-Experts model from Meta with 17 billion parameters per expert and 16 experts, activating one expert per token. It supports vision, code generation, tool calling, and 12 languages, making it one of the most versatile models in the Llama 4 family. Scout targets the efficiency-focused segment, offering multimodal capabilities at lower compute cost than dense models of similar quality. Its 10M token context window is among the largest available, and it quantizes well for self-hosted multi-GPU deployments.

At Q4_1 quantization (medium quality tier), the model weighs 62.94 GB. This fits within the 80 GB of VRAM on OVH h100-1-gpu, enabling full GPU inference.

The NVIDIA H100 80 GB is a datacenter GPU with 80 GB of HBM3 VRAM and 3350 GB/s memory bandwidth. It delivers 1979 FP16 TFLOPS on the Hopper architecture, making it the fastest single-GPU option for large language model inference. It handles models up to 70B parameters with high throughput. Built for datacenter teams running demanding production AI workloads.

Hardware Requirements

Model size	62.94 GB
VRAM available	80 GB
VRAM used	385.3 GB
System RAM	350 GB
Min RAM required	62.9 GB
GPU layers	0 / 48
Context size	2,097,152
Backend	cuda13
Flash attention	No

Performance Notes

Mixture-of-Experts

This is a Mixture-of-Experts model with 16 total experts, of which 1 are active per token. Only the active experts need fast GPU access — inactive expert weights can reside in system RAM with minimal impact on generation speed.

Read the MoE deployment guide →

Reduced performance

48 of 48 layers will run on CPU, which may reduce inference speed.

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-4-scout-17b-16e-instruct/q4_1/nvidia-h100-80gb.yaml) apply

Generated values.yaml

/values/llama-4-scout-17b-16e-instruct/q4_1/nvidia-h100-80gb.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-4-scout-17b-16e-instruct.gguf "https://huggingface.co/unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF/resolve/main/Q4_1/Llama-4-Scout-17B-16E-Instruct-Q4_1-00001-of-00002.gguf"

Start Server

llama-server \
  -m llama-4-scout-17b-16e-instruct.gguf \
  --n-gpu-layers 0 \
  --ctx-size 2097152

Verify

curl http://localhost:8080/health

Explore

• Configure a deployment
• Browse models
• Browse hardware

Other quantizations

• Llama 4 Scout 17B 16E Instruct (Q8_0)
• Llama 4 Scout 17B 16E Instruct (Q5_K_M)
• Llama 4 Scout 17B 16E Instruct (Q4_K_M)

Other hardware

• Llama 4 Scout 17B 16E Instruct on CPU Only
• Llama 4 Scout 17B 16E Instruct on NVIDIA A100 80GB
• Llama 4 Scout 17B 16E Instruct on NVIDIA RTX 3090

Frequently Asked Questions

How much VRAM does Llama 4 Scout 17B 16E Instruct (Q4_1) need?

The Q4_1 quantization of Llama 4 Scout 17B 16E Instruct requires 62.94 GB. The 80 GB of VRAM on OVH h100-1-gpu is insufficient for GPU layers, so inference runs on CPU.

Can I run Llama 4 Scout 17B 16E Instruct on OVH h100-1-gpu?

It is possible but not recommended. OVH h100-1-gpu does not have enough VRAM to accelerate Llama 4 Scout 17B 16E Instruct (Q4_1), so inference will rely on CPU and system RAM.

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_1 compresses Llama 4 Scout 17B 16E Instruct from its original size down to 62.94 GB.

What quantization should I choose for Llama 4 Scout 17B 16E Instruct?

Q4_1 is a medium-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.

Why are some layers offloaded to CPU?

OVH h100-1-gpu has 80 GB of VRAM, but Llama 4 Scout 17B 16E Instruct (Q4_1) requires approximately 62.94 GB. Only 0 of 48 layers fit in VRAM; the remaining layers run on CPU, which is slower but still functional.

What is MoE and how does it affect deployment?

Llama 4 Scout 17B 16E Instruct uses a Mixture-of-Experts (MoE) architecture with 16 experts, of which 1 are active per token. This means only a fraction of the model weights are used for each inference step, allowing MoE models to be larger in total parameter count while remaining efficient at inference time.

How do I run Llama 4 Scout 17B 16E Instruct (Q4_1) with Ollama?

Run ollama run llama4:17b-scout-16e-instruct-q4_1 to start Llama 4 Scout 17B 16E Instruct (Q4_1). Ollama handles downloading the model weights automatically on first run.

Last updated: March 20, 2026