Can I run Gemma 3 27B?

Gemma 3 27B is the model you reach for when an 8B class assistant feels too thin and you have the memory to spare. It is a 27.4B chat-and-vision model from Google, which means it reads images as well as text, and that capability is the main reason to pick it over a same-size text-only model. At a 4-bit quant it lands around 16.6 GB, so a 12 GB card like an RTX 3060 simply will not hold it, and you will want at least 24 GB of RAM or unified memory before it runs comfortably. A 24 GB RTX 4090 or an M-series Mac with plenty of unified memory is the natural home for it.

In daily use it feels like a noticeably more grounded conversationalist than the small models, with the multimodal angle making it genuinely useful for describing or reasoning over screenshots and photos. On a 4090 at 4-bit you can expect around 52 tokens per second, which streams faster than you read; an M Max Mac sits closer to 21 tok/s, still very usable, while CPU-only on DDR5 drops to roughly 3 tok/s and is really only for batch jobs. The 128K context is real but expensive here: filling it pushes total memory to about 45.8 GB, well past what a single 24 GB device holds, so keep working context modest unless you have headroom.

Against its own family it is the heavyweight, with Gemma 3 4B being the lighter pick when memory is tight and you still want vision. The more interesting comparison is Qwen 3.5 27B, a near-identical-size model that adds dedicated reasoning and coding strengths; for structured math or code tasks it generally edges Gemma out, so reach for Gemma 3 27B more for solid chat plus image understanding than for hard reasoning. Its standout trait is that vision-plus-chat balance at a manageable single-GPU size. One caveat: the Gemma license is open-weight, not open-source, so read Google's terms before shipping it in a commercial product.