gemma-3n-E2B-it

24 of 30 layers cache only a 512-token window rather than the full context, on a period of . Figures assume the default configuration; --swa-full disables the saving entirely.

A parameters × bits ÷ 8 estimate puts Q4_K_M at roughly 2.85 GiB. The real file is 2.60 GiB, because a quantization is a mixture and some tensors are always kept at higher precision. The larger discrepancy is the cache: a flat formula gives 1.88 GiB at 32K context where the real figure is 0.42 GiB, because most of this model's layers cache a fixed window rather than the whole context.

How much VRAM does gemma-3n-E2B-it need?

Q4_K_M is exactly 2,787,806,304 bytes (2.60 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.

How large is gemma-3n-E2B-it's KV cache?

0.42 GiB at 32K context with an f16 cache, computed per layer. Quantizing the cache to q8_0 roughly halves it, which is often the difference between a context length fitting and not.

Which quantization of gemma-3n-E2B-it should I use?

Q4_K_M is the usual default. Pick the largest quantization that fits your card at the context you actually need — the table above gives exact sizes for every one published.