Google Gemma 4 26B-A4B (MoE) VRAM Requirements Guide
Ultra-fast sparse Mixture-of-Experts Gemma 4 activating only 3.8B parameters per token across 30 hybrid layers. Runs comfortably on 24GB GPUs at >100 tok/s.
>98.2% retention. 14.9 GB weights, leaving +6.9 GB headroom on Dual NVIDIA RTX 3060 Rig (24GB VRAM).
Near-lossless 8-bit precision requiring 29.8 GB VRAM for benchmark evaluations.
Uncompressed FP16 weights taking 53.7 GB. Demands multi-GPU or datacenter accelerators.
Architectural Memory Breakdown & Attention Scaling
Google's Gemma 4 26B-A4B is a multimodal Mixture-of-Experts architecture containing 25.6B total parameters while activating 4.0B parameters per token across 48 layers with 5:1 Sliding Window Attention. With 8k KV-cache taking only 1.8 GiB and 4-bit Q4_K_M weights taking 14.5 GB, its 17.2 GB footprint runs at 75+ tok/s on 24GB GPUs.
In llama.cpp, pass `-fa on -ctk q8_0` to compress sliding window attention buffers. On 16GB GPUs, pass `--n-cpu-moe 8` to offload cold expert weights to DDR5 RAM while keeping active layers in VRAM.
Dual NVIDIA RTX 3060 Rig (24GB VRAM) provides 24.0 GB physical VRAM (+6.9 GB operational margin at 8k context).
Run Google Gemma 4 26B-A4B (MoE) Locally
ollama run gemma-4-26b-a4b:q4_K_Mvllm serve Google DeepMind/gemma-4-26b-a4b --max-model-len 8192 --gpu-memory-utilization 0.95Frequently Asked Questions
Can Gemma 4 26B-A4B run on a 16GB GPU?
With slight MoE expert offloading via `--n-cpu-moe 8`, it runs on 16GB cards like the RTX 4070 Ti Super at over 35 tok/s.
How does Gemma 4 26B-A4B compare to dense Gemma 4 31B?
The MoE architecture generates tokens nearly twice as fast (~75 tok/s vs ~42 tok/s) because only 4.0B parameters compute per token.
Test Custom Hardware & Context Windows
Simulate Google Gemma 4 26B-A4B (MoE) across all 31 GPUs, adjust context up to 262,144 tokens, or calculate RunPod break-even costs.
Open Google Gemma 4 26B-A4B (MoE) in GPU Garden Matchmaker →