Why Gemma 3n 4B pressures system RAM
Gemma 3n 4B is a dense 4B network — every weight participates each token, so quantization choice dominates. Q4_K_M lands near ~2.3GB weights, plus ~0.01GB KV at 8K and ~6GB overhead (~8.3GB → 16GB kit). The 33K-token context ceiling is the sleeper cost: long-doc or agent traces inflate KV while the 4B slab stays fixed. Prefer dual-channel DDR5 bandwidth when CPU offload or mmap is involved.
What RAM kit to buy
A 16GB dual-channel kit is enough for quantized Gemma 3n 4B at modest context. Still prefer 2× matched SO-DIMM/UDIMM sticks; 1x RTX 4060 Ti (16GB VRAM) or RTX 4070 (12GB VRAM) covers the Budget / Entry 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 3n 4B are dense-efficient on a single consumer GPU when quantized — system RAM still needs headroom for tokenizer, KV, and the host OS. At 4B, Gemma 3n 4B is compact enough for laptops and mini-PCs when quantized; dual-channel memory still matters for 1% token latency. Release window noted as 2025/2026; always re-check the model card before buying hardware for a specific checkpoint.