gemma-4-26B-A4B-it on 16GB card

Does gemma-4-26B-A4B-it fit in 16GB of VRAM?

Not at these settings. No indexed quantization of gemma-4-26B-A4B-it fits 16GB card at any context we compute, with q4_0 KV. The smallest shipped quantization is 25.02 GiB in weights alone, against 14.88 GiB usable. CPU offload can still run it, slowly.

From the file· weights summed from filesFrom the file· KV computed per layerPredicted· speed and compute buffer
KV cache dtypef16q8_0q4_0— 16 GB card, 14.88 GiB usable after overhead

Every quantization at every context

total memory required; green fits, red does not
QuantWeights●4K◐8K◐16K◐32K◐64K◐128K◐
F1647.04 GiB48.048.048.148.348.649.3
Q8_025.02 GiB25.926.026.126.226.627.3
●From the file◐Predictedwhat these mean

Figures are GiB of total memory: weights plus KV cache plus compute buffer and backend overhead. Weights and KV are near-exact; the overhead term is modeled. Hover any cell for the breakdown.

Why there are no speeds on this page

A capacity is not a card. Whether a model fits depends only on memory, so every figure above holds for any 16GB accelerator. How fast it runs depends on memory bandwidth, which varies several-fold between cards of the same capacity — so putting a tokens-per-second number here would be inventing one. Pick a specific card from hardware and the speed column appears.

Why other calculators disagree

A parameters × bits ÷ 8 estimate ignores two things that dominate at long context. First, the weights themselves are not the nominal rate — quantizations are mixtures, so the real file is consistently larger than the label implies. Second, most of this model's layers cache only a 1,024-token window rather than the full context.