Llama 4 Scout (109B MoE) GPU Hardware & VRAM Calculator

Meta's next-generation sparse Mixture-of-Experts (MoE) model. Activating 17B parameters per token, it brings a massive 10-million-token context window alongside native multimodal text-and-image understanding.

Quantization Precision (Bits)INT4 (Quantized)

Lower bits drastically reduce weight footprint but introduce minor accuracy degradation.

Context Length (Tokens)8,192 Tokens

Longer context windows aggressively ingest VRAM during Key-Value matrix caching.

Estimated Minimum VRAM
74 GB

Dynamically aggregated for Llama 4 Scout (109B MoE) based on your selected quantization precision and context boundary.

Scroll down for real-time mathematical proof.

📊 Real-Time VRAM Mathematical Formulation & Derivation

How did we arrive at 74 GB? Below is the verified industrial infrastructure forecasting model.

The VRAM Forecasting Equation
Total VRAM = (Model Weights + KV Cache) × System Overhead
VRAM = ((Params × Bits / 8) + (Context / 1024 × 0.5)) × 1.25

1. Input Parameters & Constants Mapping

Model Variables
  • • Params (Model Size): 109 Billion
  • • Bits (Precision): 4-bit (Selected via slider)
Runtime Constants
  • • Context Window: 8,192 Tokens (Selected via slider)
  • • KV Cache Factor: 0.5 GB / 1K Tokens (Empirical baseline)
  • • System Overhead: 1.25 (25%) (CUDA Context & Activation buffer)

2. Step-by-Step Calculation Engine

[Step 1] Compute Model Weights Allocation:

Formula: (Parameters × Bits) / 8 Bytes per GB

➔ (109B × 4) / 8 = 54.50 GB

[Step 2] Compute Key-Value (KV) Cache Matrix Size:

Formula: (Tokens / 1024) × 0.5 GB Baseline

➔ (8192 / 1024) × 0.5 = 4.00 GB

[Step 3] Apply System Overhead Risk Buffer:

Formula: (Weights + KV Cache) × 1.25 CUDA Runtime Multiplier

➔ (54.50GB + 4.00GB) × 1.25 = 73.13 GB

[Final Step] Rounding Ceiling (Ceil):⌈ 73.13 ⌉ = 74 GB

Live Cloud GPU Cost Breakdown

GPU HardwareRequired Cluster SizeCombined VRAMEstimated CostDeployment Link
NVIDIA Blackwell B2001x Node192 GB$4.85/hrRent via RunPod ↗
NVIDIA Hopper H200 141GB1x Node141 GB$2.95/hrRent via RunPod ↗
NVIDIA H100 SXM 80GB1x Node80 GB$2.19/hrRent via RunPod ↗
NVIDIA H100 PCIe 80GB1x Node80 GB$1.75/hrRent via RunPod ↗
NVIDIA A100 SXM 80GB1x Node80 GB$1.35/hrRent via RunPod ↗
NVIDIA A10G 24GB4x Node96 GB$3.16/hrRent via RunPod ↗
NVIDIA L4 24GB4x Node96 GB$2.20/hrRent via RunPod ↗
NVIDIA RTX 4090 24GB4x Node96 GB$2.60/hrRent via RunPod ↗
NVIDIA RTX 3090 24GB4x Node96 GB$1.56/hrRent via RunPod ↗
AMD Instinct MI300X1x Node192 GB$2.65/hrRent via RunPod ↗
NVIDIA RTX 5090 32GB3x Node96 GB$4.74/hrRent via RunPod ↗
NVIDIA H100 NVL 94GB1x Node94 GB$3.19/hrRent via RunPod ↗
NVIDIA L40S 48GB2x Node96 GB$3.80/hrRent via RunPod ↗
NVIDIA RTX 6000 Ada 48GB2x Node96 GB$4.18/hrRent via RunPod ↗
NVIDIA RTX A6000 48GB2x Node96 GB$2.44/hrRent via RunPod ↗
NVIDIA A100 PCIe 80GB1x Node80 GB$1.19/hrRent via RunPod ↗
NVIDIA RTX A5000 24GB4x Node96 GB$1.08/hrRent via RunPod ↗
NVIDIA RTX Pro 6000 96GB1x Node96 GB$2.09/hrRent via RunPod ↗
NVIDIA A40 48GB2x Node96 GB$0.88/hrRent via RunPod ↗
NVIDIA L40 48GB2x Node96 GB$1.38/hrRent via RunPod ↗
NVIDIA A100 PCIe 40GB2x Node80 GB$1.20/hrRent via RunPod ↗
NVIDIA RTX 4000 Ada 24GB4x Node96 GB$1.80/hrRent via RunPod ↗
NVIDIA RTX A4000 16GB5x Node80 GB$1.15/hrRent via RunPod ↗
AMD Instinct MI210 64GB2x Node128 GB$1.50/hrRent via RunPod ↗

Pros & Cons of Llama 4 Scout (109B MoE)

PROS
  • Astounding 10M context length handler
  • Native multimodal vision intelligence
  • Highly cost-effective inference via sparse MoE routing
CONS
  • Requires optimized memory paging to manage massive long-context state overhead

Production Deployment Guide

# Option 1: Quick Local Deployment via Ollamaollama run llama4:scout
# Option 2: High-Throughput Cluster via vLLMpython -m vllm.entrypoints.openai.api_server --model meta-llama/Llama-4-Scout-17B-16E-Instruct --tensor-parallel-size 4