Why Gemma 4 26B (MoE) pressures system RAM
Gemma 4 26B (MoE) is Mixture-of-Experts: inference activates 3.8B active/token, but VRAM/RAM must usually hold the full ~26B expert set for fast routing. At Q4 the weight slab is ~14.6GB before KV (~0.01GB at 8K) and ~6GB OS/runtime overhead — totaling ~20.6GB raw, rounded to a 32GB kit. Stretching toward the full 131K-token window multiplies KV far faster than weights; that is the usual “I bought enough RAM for the model but still OOM” failure on Google Gemma MoE pages.
What RAM kit to buy
A 32GB dual-channel kit is enough for quantized Gemma 4 26B (MoE) at modest context. Still prefer 2× matched SO-DIMM/UDIMM sticks; 1x RTX 3090 or RTX 4090 (24GB VRAM) covers the Flagship Consumer GPU GPU profile. If you chat with long pastes, jump a tier before the KV cache forces paging.
Workload notes
Gemma-class models like Gemma 4 26B (MoE) are dense-efficient on a single consumer GPU when quantized — system RAM still needs headroom for tokenizer, KV, and the host OS. At 26B, Gemma 4 26B (MoE) is a practical mid-size local model — sweet spot for single-GPU Q4/Q8 experimenters who still want headroom for IDE + Docker. Release window noted as April 2026; always re-check the official source before buying hardware for a specific checkpoint.