Gemma 3

Multimodal architecture – Gemma 3 incorporates a frozen SigLIP vision encoder, condensing images into 256 “soft tokens.” A new Pan & Scan (P&S) method better handles images of varying aspect ratios by splitting them into crops at inference, improving tasks like document QA or text recognition. Use it to analyze images, text, and short videos.

Up to 128K context length – By interleaving local (sliding-window) and global attention layers (5:1 ratio), Gemma 3 curbs the explosive KV-cache memory usage typical of longer contexts. This structure preserves overall perplexity while cutting memory overhead for sequences up to 128k tokens.

Knowledge distillation & quantization – The model uses advanced teacher-student distillation and is further refined with quantization-aware training (QAT). Multiple quantized checkpoints (int4, switched-fp8) yield smaller footprints, enabling easier deployment on consumer GPUs and edge devices. Gemma 3 can fit on a single GPU or TPU host.

Instruction-tuned performance – After post-training with specialized reward signals (for math, coding, multilingual chat), Gemma 3 IT significantly outperforms previous Gemma 2 across benchmarks like MMLU, coding (HumanEval), and chat-based evaluations. Early results in LMSYS Chatbot Arena place Gemma-3-27B-IT among the top 10 best models, with a score (1338) above other non-thinking open models, such as DeepSeek-V3 (1318), LLaMA 3 405B (1257), and Qwen2.5-70B (1257).

140 languages and advanced workflows- Gemma 3 supports 35 languages out-of-the-box and pretrained to support over 140 languages. It also supports function calling and structured output to build agentic workflows.

Safety, privacy, and memorization – Focused data filtering and decontamination reduce exact memorization rates. Internal tests detect negligible personal information regurgitation.