WizardLM-2 8x22B RAM Calculator
WizardLM-2 8x22B is Microsoft AI's most advanced Wizard model. It demonstrates highly competitive performance compared to leading proprietary models, and it consistently outperforms all existing state-of-the-art opensource models. It is...
128GB 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
10.2 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.
128GB System RAM
Stably runs model with Q4/Q8/FP16 quantization & target context windows.
Memory Allocation Sizer
Dynamic Dual-Channel Picks (128GB)
A-Tech 128GB Kit (4x32GB) RAM for Apple iMac 2019 & 2020 27 inch Retina 5K | DDR4 2666 MHz SODIMM PC4-21300 / PC4-21333 260-Pin SO-DIMM Max Memory Upgrade
A-Tech 128GB Kit (4x32GB) DDR5 4800MHz PC5-38400 CL40 SODIMM 2Rx8 Dual Rank 1.1V Non-ECC Unbuffered SO-DIMM 262-Pin Laptop Computer RAM Memory Upgrade Modules
Corsair Vengeance LPX 128GB (4x32GB) DDR4 3600 (PC4-28800) C18 1.35V Desktop Memory - Black
128GB (4X32GB) DDR4 2666MHZ PC4-21300 UDIMM 2Rx8 1.2V CL19 288-PIN Non-ECC Unbuffered Desktop PC Computer 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.
WizardLM-2 8x22B Memory FAQs
How much RAM does WizardLM-2 8x22B require?
To run WizardLM-2 8x22B locally, memory size depends on your selected quantization. At 4-bit compression (Q4_K_M), the weights take up ~99GB of RAM. When combined with context cache and OS overhead, a standard **128GB system memory kit** is recommended. Unquantized FP16 execution requires a **384GB memory setup**.
What are the hardware requirements to run WizardLM-2 8x22B at FP16 precision?
Running WizardLM-2 8x22B at unquantized 16-bit precision requires loading ~352GB of model weights directly into VRAM or system memory. A minimum system memory target of **384GB RAM** is required to run the weights stably and avoid out-of-memory crashes.
Is WizardLM-2 8x22B dense or Mixture-of-Experts (MoE)?
WizardLM-2 8x22B is built on a **MoE** architecture. It has a total of 176 Billion parameters, but only activates 44 Billion parameters per token. While active parameter sparse execution makes token computing very fast, the entire 176B parameters must reside in VRAM/RAM for fast expert switching during execution.
How does context window size affect RAM usage for WizardLM-2 8x22B?
Context size directly scales the size of the Key-Value (KV) cache. At a standard 8,192 token context, the KV cache for WizardLM-2 8x22B uses ~0.11GB. If you scale this to 65.536K tokens, the KV cache can scale past hundreds of GBs, requiring multi-GPU or workstation-class RAM configurations.