How much VRAM does Gemma-4-Giftige-Blume-31B-v2 need?

Its published quantizations range from 16.54 GiB to 30.39 GiB in weights alone, before KV cache and runtime overhead.

Gemma-4-Giftige-Blume-31B-v2 on Apple M3 Pro

Can I run Gemma-4-Giftige-Blume-31B-v2 on a Apple M3 Pro?

Q: Can I run Gemma-4-Giftige-Blume-31B-v2 on a Apple M3 Pro?

No. No published quantization of Gemma-4-Giftige-Blume-31B-v2 fits a Apple M3 Pro at any context with q4_0 KV cache.

Not at these settings. No indexed quantization of Gemma-4-Giftige-Blume-31B-v2 fits Apple M3 Pro at any context we compute, with q4_0 KV. The smallest shipped quantization is 16.54 GiB in weights alone, against 12.56 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 dtypef16 q8_0 q4_0— 18 GB card, 12.56 GiB usable after overhead

Every quantization at every context

total memory required; green fits, red does not

Quant	Weights●	4K◐	8K◐	16K◐	32K◐	64K◐	128K◐
Q8_0	30.39 GiB	31.5	31.7	32.1	32.8	34.2	37.0
Q6_K	23.47 GiB	24.6	24.8	25.1	25.8	27.2	30.1
Q5_K_M	20.35 GiB	21.5	21.7	22.0	22.7	24.1	26.9
Q5_K_S	19.85 GiB	21.0	21.2	21.5	22.2	23.6	26.4
Q4_K_M	17.40 GiB	18.5	18.7	19.1	19.8	21.2	24.0
Q4_K_S	16.54 GiB	17.7	17.9	18.2	18.9	20.3	23.1

●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 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.