Gemma 3.5 27B: best open-weight multimodal model

Gemma 3.5 27B multimodal model illustration

27B is an interesting size: too big for small cards, comfortable on big ones. At 4-bit quantization you are looking at roughly 16 to 18GB of video memory (VRAM), which means a 24GB card runs it with room to spare while a 16GB card leans on partial offloading to system RAM.

That offloading works, but every layer that spills to RAM costs speed. The practical question is what you give up by dropping to a smaller model. For general chat and writing, the difference between 27B and 12B is real but not dramatic. For reasoning-heavy work, the bigger model earns its VRAM.

Context length is the hidden cost at this size. A 27B model with a 128k-token window needs several extra gigabytes for the key-value cache, so a 24GB card that fits the weights may still feel tight with long documents. Check both numbers.

If you are buying hardware for this class, 24GB is the sweet spot. It covers 27B at 4-bit plus context headroom, and it keeps 32B models in reach. Run the exact numbers for your setup.

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