Gemma 4 26B (MoE) RAM Calculator
Ultra-efficient sparse MoE model from Google DeepMind, activating just 3.8 Billion parameters per token. Ideal for fast local inference and constrained hardware environments.
32GB 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
3.1 t/s
DDR5 CPU Mode
96 GB/s
6.6 t/s
Mac Unified Memory
300 GB/s
20.5 t/s
GPU VRAM (RTX 4090)
1008 GB/s
69.0 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.
32GB System RAM
Stably runs model with Q4/Q8/FP16 quantization & target context windows.
Memory Allocation Sizer
Dynamic Dual-Channel Picks (32GB)
CORSAIR Vengence RGB DDR5 RAM 32GB (2x16GB) 6000MHz CL36-44-44-96 1.35V Intel XMP 3.0 Computer Memory – Black (CMH32GX5M2E6000C36)
CORSAIR VENGEANCE RGB DDR5 RAM 32GB (2x16GB) 6000MHz CL36-44-44-96 1.35V Intel XMP 3.0 Desktop Computer Memory - White (CMH32GX5M2E6000C36W)
Patriot Viper Steel DDR4 RAM 32GB (2X16GB) 3600MHz CL18 1.35v UDIMM Desktop Gaming Memory Kit Compatible with XMP - PVS432G360C8K
Fury Beast 32GB (2x16GB) 3600MT/s DDR4 CL18 Desktop Memory Kit of 2 KF436C18BBK2/32
Technical Specifications
GPU & VRAM Sizing Profile
Flagship Consumer GPUHardware Profile: The consumer gold standard. Allows 100% GPU acceleration on a single card, delivering blazing-fast token generation.
Gemma 4 26B (MoE) Memory FAQs
How much RAM does Gemma 4 26B (MoE) require?
To run Gemma 4 26B (MoE) locally, memory size depends on your selected quantization. At 4-bit compression (Q4_K_M), the weights take up ~14.6GB of RAM. When combined with context cache and OS overhead, a standard **32GB system memory kit** is recommended. Unquantized FP16 execution requires a **64GB memory setup**.
What are the hardware requirements to run Gemma 4 26B (MoE) at FP16 precision?
Running Gemma 4 26B (MoE) at unquantized 16-bit precision requires loading ~52GB of model weights directly into VRAM or system memory. A minimum system memory target of **64GB RAM** is required to run the weights stably and avoid out-of-memory crashes.
Is Gemma 4 26B (MoE) dense or Mixture-of-Experts (MoE)?
Gemma 4 26B (MoE) is built on a **MoE** architecture. It has a total of 26 Billion parameters, but only activates 3.8 Billion parameters per token. While active parameter sparse execution makes token computing very fast, the entire 26B parameters must reside in VRAM/RAM for fast expert switching during execution.
How does context window size affect RAM usage for Gemma 4 26B (MoE)?
Context size directly scales the size of the Key-Value (KV) cache. At a standard 8,192 token context, the KV cache for Gemma 4 26B (MoE) uses ~0.01GB. If you scale this to 131.072K tokens, the KV cache can scale past hundreds of GBs, requiring multi-GPU or workstation-class RAM configurations.