Llama 4 (Scout & Maverick)

Run Meta Llama 4 Scout & Maverick MoE models on Clore.ai GPUs

Meta's Llama 4, released April 2025, marks a fundamental shift to Mixture of Experts (MoE) architecture. Instead of activating all parameters for every token, Llama 4 routes each token to specialized "expert" sub-networks — delivering frontier performance at a fraction of the compute cost. Two open-weight models are available: Scout (ideal for single-GPU) and Maverick (multi-GPU powerhouse).

Key Features

• MoE Architecture: Only 17B parameters active per token (out of 109B/400B total)

• Massive Context Windows: Scout supports 10M tokens, Maverick supports 1M tokens

• Natively Multimodal: Understands both text and images out of the box

• Two Models: Scout (16 experts, single-GPU friendly) and Maverick (128 experts, multi-GPU)

• Competitive Performance: Scout matches Gemma 3 27B; Maverick competes with GPT-4o class models

• Open Weights: Llama Community License (free for most commercial uses)

Model Variants

Model	Total Params	Active Params	Experts	Context	Min VRAM (Q4)	Min VRAM (FP16)
Scout	109B	17B	16	10M	12GB	80GB
Maverick	400B	17B	128	1M	48GB (multi)	320GB (multi)

Requirements

Component	Scout (Q4)	Scout (FP16)	Maverick (Q4)
GPU	1× RTX 4090	1× H100	4× RTX 4090
VRAM	24GB	80GB	4×24GB
RAM	32GB	64GB	128GB
Disk	50GB	120GB	250GB
CUDA	12.8+	12.8+	12.8+

Recommended Clore.ai GPU: RTX 4090 24GB ($0.14–0.42/hr) for Scout — best value

Quick Start with Ollama

The fastest way to get Llama 4 running:

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Run Scout (quantized, ~12GB VRAM)
ollama run llama4-scout

# For longer context (uses more VRAM)
ollama run llama4-scout --ctx-size 32768

Ollama as API Server

# Start server in background
ollama serve &

# Pull model
ollama pull llama4-scout

# Query via OpenAI-compatible API
curl http://localhost:11434/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama4-scout",
    "messages": [{"role": "user", "content": "Explain MoE architecture in 3 sentences"}]
  }'

vLLM Setup (Production)

For production workloads with higher throughput:

# Install vLLM
pip install vllm

# Serve Scout on single GPU (quantized)
vllm serve meta-llama/Llama-4-Scout-17B-16E-Instruct \
  --max-model-len 32768 \
  --gpu-memory-utilization 0.90

# Serve Scout on 2 GPUs (longer context)
vllm serve meta-llama/Llama-4-Scout-17B-16E-Instruct \
  --tensor-parallel-size 2 \
  --max-model-len 128000 \
  --gpu-memory-utilization 0.90

# Serve Maverick on 4 GPUs
vllm serve meta-llama/Llama-4-Maverick-17B-128E-Instruct \
  --tensor-parallel-size 4 \
  --max-model-len 65536

Query vLLM Server

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="dummy")

response = client.chat.completions.create(
    model="meta-llama/Llama-4-Scout-17B-16E-Instruct",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Write a Python function to calculate Fibonacci numbers"}
    ],
    temperature=0.7,
    max_tokens=1024
)
print(response.choices[0].message.content)

HuggingFace Transformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "meta-llama/Llama-4-Scout-17B-16E-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    load_in_4bit=True  # 4-bit quantization for 24GB GPUs
)

messages = [
    {"role": "system", "content": "You are a helpful coding assistant."},
    {"role": "user", "content": "Write a REST API with FastAPI that manages a todo list"}
]

input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
output = model.generate(input_ids, max_new_tokens=2048, temperature=0.7, do_sample=True)
print(tokenizer.decode(output[0][input_ids.shape[-1]:], skip_special_tokens=True))

Docker Quick Start

# Using vLLM Docker image
docker run --gpus all -p 8000:8000 \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  vllm/vllm-openai:latest \
  --model meta-llama/Llama-4-Scout-17B-16E-Instruct \
  --max-model-len 32768

Why MoE Matters on Clore.ai

Traditional dense models (like Llama 3.3 70B) need massive VRAM because all 70B parameters are active. Llama 4 Scout has 109B total but only activates 17B per token — meaning:

• Same quality as 70B+ dense models at a fraction of the VRAM cost

• Fits on a single RTX 4090 in quantized mode

• 10M token context — process entire codebases, long documents, books

• Cheaper to rent — one RTX 4090 at $0.14–0.42/hr instead of a multi-GPU rig for 70B models

Tips for Clore.ai Users

• Start with Scout Q4: Best bang for buck on RTX 4090 — $0.14–0.42/hr, covers 95% of use cases

• Use --max-model-len wisely: Don't set context higher than you need — it reserves VRAM. Start at 8192, increase as needed

• Tensor Parallel for Maverick: Rent 4× RTX 4090 machines for Maverick; use --tensor-parallel-size 4

• HuggingFace Login Required: huggingface-cli login — you need to accept the Llama license on HF first

• Ollama for Quick Tests, vLLM for Production: Ollama is faster to set up; vLLM gives higher throughput for API serving

• Monitor GPU Memory: watch nvidia-smi — MoE models can spike VRAM on long sequences

Troubleshooting

Issue	Solution
OutOfMemoryError	Reduce --max-model-len, use Q4 quantization, or upgrade GPU
Model download fails	Run huggingface-cli login and accept Llama 4 license at hf.co
Slow generation	Ensure GPU is being used (nvidia-smi); check --gpu-memory-utilization
vLLM crashes on start	Reduce context length; ensure CUDA 11.8+ installed
Ollama shows wrong model	Run ollama list to verify; ollama rm + ollama pull to re-download

Further Reading

• Meta Llama 4 Blog Post

• HuggingFace Model Card

• vLLM Documentation

• Ollama Model Library