# Meta LLaMA 4 Scout (109B / 17B Active MoE) VRAM Requirements & Hardware Sizing Guide

> **Quick Summary (Answer Capsule)**: Running Meta LLaMA 4 Scout (109B / 17B Active MoE) requires 67.78 GB at Q4_K_M (4-bit), 122.69 GB at Q8_0 (8-bit), and 225.76 GB at FP16. Recommended hardware sweet spot: AMD Ryzen AI Max+ 395 Strix Halo 128GB. Scaling context past 32k tokens requires enabling FP8 KV-cache (-ctk q8_0) to prevent CUDA allocation failures.

## Memory Specifications by Precision
| Quantization Format | Precision Level | Required VRAM (8k context) | Quality Retention | Minimum Recommended GPU |
|---|---|---|---|---|
| 4-Bit (Q4_K_M) | Recommended Default | 67.78 GB | >98.2% Perplexity | AMD Ryzen AI Max+ 395 Strix Halo 128GB |
| 8-Bit (Q8_0) | Near Lossless | 122.69 GB | ~99.9% Perplexity | Workstation / High-Tier GPU |
| 16-Bit (FP16/BF16) | Uncompressed Full | 225.76 GB | 100% Fidelity | Multi-GPU / Datacenter |

## Architectural Memory Analysis
Meta LLaMA 4 Scout 109B is an enterprise Mixture-of-Experts architecture containing 109B total parameters while activating 17B parameters per token across 64 layers with 16 GQA key-value heads. Consuming ~65 GB at Q4_K_M precision, it is engineered for high-concurrency enterprise serving on dual 48GB rigs, AMD Strix Halo 128GB, or datacenter cloud nodes.

## Tier 1 Free Software Optimization ($0)
Deploy using vLLM with `--tensor-parallel-size 2 --kv-cache-dtype fp8`. On multi-GPU nodes, ensure NCCL peer-to-peer communication is enabled across PCIe or NVLink bridges to prevent inter-GPU latency bottlenecks.

## 1-Click Launch Terminal Snippets
- Ollama: `ollama run llama-4-scout-109b:q4_K_M`
- vLLM: `vllm serve Meta AI/llama-4-scout-109b --max-model-len 8192 --gpu-memory-utilization 0.95`

## Verified Hardware & Cloud Pathways
- Amazon Hardware: [Ryzen AI Max+ 395 128GB mini PC](https://www.amazon.com/s?k=Ryzen+AI+Max%2B+395+128GB+mini+PC&tag=sparecables-20) (rel="noopener sponsored")
- RunPod Cloud Rental: [Deploy Instance](https://runpod.io?ref=ynypzu94) (rel="noopener sponsored")
- Vast.ai Cloud Rental: [Deploy Instance](https://cloud.vast.ai/?ref_id=723130) (rel="noopener sponsored")

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*Interactive Sizing Calculator available at: https://www.gpugarden.com/vram/llama-4-scout-109b/*