Llama 4 Scout (109B MoE) RAM Calculator
Meta's long-context open-weights champion featuring a native 10 Million token window. Alternates dense and MoE layers to fit on prosumer developer machines with quantization.
96GB RAM
Calculated for 4-bit (Q4_K_M) @ 8K Context1. Select Quantization Level
Quantization compresses model weights to reduce RAM usage, with minor impacts on output quality.
2. Set Target Context Length
Longer contexts require more active memory for the Key-Value (KV) cache.
Inference Bandwidth & Speed Matrix
Estimates generation speeds (tokens per second) based on physical memory channel bandwidth constraints.
DDR4 CPU Mode
45 GB/s
0.7 t/s
DDR5 CPU Mode
96 GB/s
1.6 t/s
Mac Unified Memory
300 GB/s
4.9 t/s
GPU VRAM (RTX 4090)
1008 GB/s
16.4 t/s
*Token throughput calculated strictly from weight volume transfers over memory channels. Actual generation speeds can be further throttled by processing threads or VRAM offloading parameters.
96GB System RAM
Stably runs model with Q4/Q8/FP16 quantization & target context windows.
Memory Allocation Sizer
Dynamic Dual-Channel Picks (96GB)
CORSAIR Vengeance RGB DDR5 RAM 96GB (2x48GB) 6000MHz CL30 Intel XMP iCUE Compatible Computer Memory - Black (CMH96GX5M2B6000C30)
A-Tech 96GB Kit (2x48GB) DDR5 5600MHz PC5-44800 CL46 SODIMM 2Rx8 Dual Rank 1.1V Non-ECC Unbuffered SO-DIMM 262-Pin Laptop Computer RAM Memory Upgrade Modules
CORSAIR Vengeance DDR5 RAM 96GB (2x48GB) 6000MHz CL36-44-44-96 1.4V AMD Expo Intel XMP 3.0 Desktop Computer Memory – Gray (CMK96GX5M2E6000Z36)
Vengeance RGB DDR5 RAM 96GB (2x48GB) 6000MHz CL36-44-44-96 1.4V AMD Expo Intel XMP 3.0 Desktop Computer Memory – Gray (CMH96GX5M2E6000Z36)
Technical Specifications
GPU & VRAM Sizing Profile
Multi-GPU Workstation / Mac StudioHardware Profile: Advanced workspace setup. Apple Silicon Mac Studios with Unified Memory provide a massive cost-saving advantage here by utilizing pooled high-bandwidth shared RAM.
Llama 4 Scout (109B MoE) Memory FAQs
How much RAM does Llama 4 Scout (109B MoE) require?
To run Llama 4 Scout (109B MoE) locally, memory size depends on your selected quantization. At 4-bit compression (Q4_K_M), the weights take up ~61.3GB of RAM. When combined with context cache and OS overhead, a standard **96GB system memory kit** is recommended. Unquantized FP16 execution requires a **256GB memory setup**.
What are the hardware requirements to run Llama 4 Scout (109B MoE) at FP16 precision?
Running Llama 4 Scout (109B MoE) at unquantized 16-bit precision requires loading ~218GB of model weights directly into VRAM or system memory. A minimum system memory target of **256GB RAM** is required to run the weights stably and avoid out-of-memory crashes.
Is Llama 4 Scout (109B MoE) dense or Mixture-of-Experts (MoE)?
Llama 4 Scout (109B MoE) is built on a **MoE** architecture. It has a total of 109 Billion parameters, but only activates 17 Billion parameters per token. While active parameter sparse execution makes token computing very fast, the entire 109B parameters must reside in VRAM/RAM for fast expert switching during execution.
How does context window size affect RAM usage for Llama 4 Scout (109B MoE)?
Context size directly scales the size of the Key-Value (KV) cache. At a standard 8,192 token context, the KV cache for Llama 4 Scout (109B MoE) uses ~0.04GB. If you scale this to 1 Million tokens, the KV cache can scale past hundreds of GBs, requiring multi-GPU or workstation-class RAM configurations.