Llama 4 Maverick (400B MoE) RAM Calculator
Meta's premier large-scale open-weight Mixture-of-Experts flagship. Runs highly advanced logic, multimodal calculations, and deep code generation.
256GB 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
0.5 t/s
DDR5 CPU Mode
96 GB/s
1.0 t/s
Mac Unified Memory
300 GB/s
3.0 t/s
GPU VRAM (RTX 4090)
1008 GB/s
5.0 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.
256GB System RAM
Stably runs model with Q4/Q8/FP16 quantization & target context windows.
Memory Allocation Sizer
Dynamic Dual-Channel Picks (256GB)
A-Tech 256GB Kit (8x32GB) DDR4 2666MHz PC4-21300 ECC RDIMM 2Rx4 Dual Rank 1.2V ECC Registered DIMM 288-Pin Server & Workstation RAM Memory Upgrade Modules (A-Tech Enterprise Series)
A-Tech 256GB Kit (8x32GB) DDR4 2133MHz PC4-17000 ECC RDIMM 2Rx4 Dual Rank 1.2V ECC Registered DIMM 288-Pin Server & Workstation RAM Memory Upgrade Modules (A-Tech Enterprise Series)
A-Tech 256GB Kit (8x32GB) DDR4 2400MHz PC4-19200 ECC RDIMM 2Rx4 Dual Rank 1.2V ECC Registered DIMM 288-Pin Server & Workstation RAM Memory Upgrade Modules (A-Tech Enterprise Series)
NEMIX RAM 256GB (8X32GB) DDR4 2933MHZ PC4-23400 2Rx4 1.2V CL21 288-PIN ECC RDIMM Registered Server Memory KIT
Technical Specifications
GPU & VRAM Sizing Profile
Enterprise GPU Node / Mac Studio 192GBHardware Profile: Server-scale deployment. Running this model locally requires extreme unified memory Apple systems or professional multi-GPU servers.
Llama 4 Maverick (400B MoE) Memory FAQs
How much RAM does Llama 4 Maverick (400B MoE) require?
To run Llama 4 Maverick (400B MoE) locally, memory size depends on your selected quantization. At 4-bit compression (Q4_K_M), the weights take up ~225GB of RAM. When combined with context cache and OS overhead, a standard **256GB system memory kit** is recommended. Unquantized FP16 execution requires a **1024GB memory setup**.
What are the hardware requirements to run Llama 4 Maverick (400B MoE) at FP16 precision?
Running Llama 4 Maverick (400B MoE) at unquantized 16-bit precision requires loading ~800GB of model weights directly into VRAM or system memory. A minimum system memory target of **1024GB RAM** is required to run the weights stably and avoid out-of-memory crashes.
Is Llama 4 Maverick (400B MoE) dense or Mixture-of-Experts (MoE)?
Llama 4 Maverick (400B MoE) is built on a **MoE** architecture. It has a total of 400 Billion parameters, but only activates 17 Billion parameters per token. While active parameter sparse execution makes token computing very fast, the entire 400B parameters must reside in VRAM/RAM for fast expert switching during execution.
How does context window size affect RAM usage for Llama 4 Maverick (400B 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 Maverick (400B 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.