Meta AI 109.0 Billion Parameters (MOE LLM)

Meta LLaMA 4 Scout (109B / 17B Active MoE) VRAM Requirements Guide

Native multimodal 16-expert MoE (109B total, 17B active). Requires ~65GB VRAM at Q4_K_M (Dual 5090 64GB, Mac 96GB+, or A100 80GB).

4-Bit (Q4_K_M) - Recommended

67.78 GB

>98.2% retention. 64.4 GB weights, leaving +28.2 GB headroom on AMD Ryzen AI Max+ 395 Strix Halo 128GB.

8-Bit (Q8_0) - Near Lossless

122.69 GB

Near-lossless 8-bit precision requiring 122.7 GB VRAM for benchmark evaluations.

16-Bit (FP16 / BF16) - Full

225.76 GB

Uncompressed FP16 weights taking 225.8 GB. Demands multi-GPU or datacenter accelerators.

Architectural Memory Breakdown & Attention Scaling

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.

Recommended Hardware Sweet Spot

AMD Ryzen AI Max+ 395 Strix Halo 128GB

AMD Ryzen AI Max+ 395 Strix Halo 128GB provides 128.0 GB physical VRAM (+28.2 GB operational margin at 8k context).

Buy AMD Ryzen AI Max+ 395 Strix Halo 128GB on Amazon Rent Cloud GPU on RunPod (from $0.79/hr) Rent on Vast.ai (from $0.35/hr)

Run Meta LLaMA 4 Scout (109B / 17B Active MoE) Locally

Ollama Execution Command:

ollama run llama-4-scout-109b:q4_K_M

vLLM Production Serving:

vllm serve Meta AI/llama-4-scout-109b --max-model-len 8192 --gpu-memory-utilization 0.95

Frequently Asked Questions

Can LLaMA 4 Scout 109B run on local consumer hardware?

At ~65 GB VRAM, it requires Dual RTX 5090 (64GB) with slight quantization, AMD Strix Halo 128GB, or an Apple Mac Studio 128GB.

Why is LLaMA 4 Scout called an MoE architecture?

While possessing 109B total parameters, only 17B parameters activate per token, allowing fast generation speeds while maintaining the reasoning breadth of a 100B+ model.

Test Custom Hardware & Context Windows

Simulate Meta LLaMA 4 Scout (109B / 17B Active MoE) across all 31 GPUs, adjust context up to 131,072 tokens, or calculate RunPod break-even costs.

Open Meta LLaMA 4 Scout (109B / 17B Active MoE) in GPU Garden Matchmaker →