Meta Llama 4 Scout & Llama 3.1 405B: Next-Generation Open Weights Intelligence

Overview #

A technical evaluation of Meta's upcoming Llama 4 Scout architecture and the flagship Llama 3.1 405B model, detailing Mixture-of-Experts routing, massive cluster pretraining, and sovereign self-hosting.

Llama 4 Scout: Meta's Pivot to Mixture-of-Experts (MoE) #

Building upon dense Llama 3 architectures, Meta's Llama 4 Scout introduces native Mixture-of-Experts (MoE) routing. By activating only a sparse subset of expert parameters per token, Llama 4 Scout delivers 3x faster inference throughput and sub-cent API pricing while maintaining 400B+ parameter intellectual depth.

Llama 3.1 405B: The Global Teacher Model #

Llama 3.1 405B remains the world's premier open foundation model for synthetic data generation, automated grading, and knowledge distillation. Trained across 16,000 H100 GPUs, it matches GPT-4o across competitive coding and graduate-level reasoning benchmarks.

Complete Data Sovereignty & Air-Gapped Deployments #

Because Meta provides open model weights, defense contractors, healthcare networks, and national governments deploy Llama models within private, air-gapped on-premise VPCs with zero data transmission to external vendors.

Code Example: Querying Llama 3.1 405B via API100 Gateway #

from openai import OpenAI

client = OpenAI(base_url="https://api.apihundred.com/v1", api_key="your_api100_key")

response = client.chat.completions.create(
    model="llama-3.1-405b-instruct",
    messages=[
        {"role": "system", "content": "You are a research mathematician."},
        {"role": "user", "content": "Explain the Langlands program and its connection to modular forms."}
    ],
    temperature=0.2
)

print(response.choices[0].message.content)

Frequently Asked Questions #

Q: What is Llama 4 Scout?

Llama 4 Scout is Meta's next-generation open architecture model utilizing sparse Mixture-of-Experts (MoE) to deliver faster inference speeds at lower serving costs.

Q: How many GPUs are required to run Llama 3.1 405B locally?

Unquantized FP16 requires at least 8x 80GB H100 or A100 GPUs (approximately 800 GB of VRAM). 4-bit quantized versions can run on a single 8-GPU node with 384 GB of aggregate memory.

Q: Can I fine-tune Llama models on private company data?

Yes, using LoRA (Low-Rank Adaptation) or full parameter tuning, organizations can adapt Llama weights to their proprietary database schemas and internal documentation.