Welcome Gemma 4: Frontier multimodal intelligence on device

The Gemma 4 family of multimodal models by Google DeepMind is out on Hugging Face, with support for your favorite agents, inference engines, and fine-tuning libraries 🤗

These models are the real deal: truly open with Apache 2 licenses, high quality with pareto frontier arena scores, multimodal including audio, and sizes you can use everywhere including on-device. Gemma 4 builds on advances from previous families and makes them click together. In our tests with pre-release checkpoints we have been impressed by their capabilities, to the extent that we struggled to find good fine-tuning examples because they are so good out of the box.

We collaborated with Google and the community to make them available everywhere: transformers, llama.cpp, MLX, WebGPU, Rust; you name it. This blog post will show you how to build with your favorite tools so let us know what you think!

Gemma 4 leverages several architecture components used in previous Gemma versions and other open models, and leaves out complex or inconclusive features such as Altup. The combination is a mix designed to be highly compatible across libraries and devices, that can efficiently support long context and agentic use cases, whilst being ideal for quantization.

As shown in the benchmarks above, this feature mix (combined with the training data and recipe) enables the 31B dense model to achieve an estimated LMArena score (text only) of 1452, while the 26B MoE reaches 1441 with just 4B active parameters 🤯. As we'll see, multimodal operation is comparatively as good as text generation, at least in informal and subjective tests.

One of the most distinctive features in smaller Gemma 4 models is Per-Layer Embeddings (PLE), which was introduced previously in Gemma-3n. In a standard transformer, each token gets a single embedding vector at input, and the same initial representation is what the residual stream builds on across all layers, forcing the embedding to frontload everything the model might need. PLE adds a parallel, lower-dimensional conditioning pathway alongside the main residual stream. For each token, it produces a small dedicated vector for every layer by combining two signals: a token-identity component (from an embedding lookup) and a context-aware component (from a learned projection of the main embeddings). Each decoder layer then uses its corresponding vector to modulate the hidden states via a lightweight residual block after attention and feed-forward. This gives each layer its own channel to receive token-specific information only when it becomes relevant, rather than requiring everything to be packed into a single upfront embedding. Because the PLE dimension is much smaller than the main hidden size, this adds meaningful per-layer specialization at modest parameter cost. For multimodal inputs (images, audio, video), PLE is computed before soft tokens are merged into the embedding sequence — since PLE relies on token IDs that are lost once multimodal features replace the placeholders. Multimodal positions use the pad token ID, effectively receiving neutral per-layer signals.

The Gemma 4 12B Unified model has no separate vision or audio encoder. Instead, raw image patches and audio waveforms are projected directly into the LLM's embedding space through lightweight linear layers, and all modalities flow into a single decoder-only transformer. This reduces multimodal latency and allows the whole model to be fine-tuned in one pass. The checkpoint size makes it deployment-friendly on consumer hardware.

Gemma 4 comes with day-0 support for many open-source inference engines, and is ideal for tool calling and agents! We also release ONNX checkpoints that can run on many hardware backends, allowing use cases on edge devices or in browser!

Similar to Gemma-3n, Gemma 4 supports image, text, and audio inputs, and generates text responses. The text decoder is based on the Gemma model with support for long context windows. The image encoder is similar to the one from Gemma 3 but with two crucial improvements: variable aspect ratios, and configurable number of image token inputs to find your sweet spot between speed, memory, and quality. All models support images (or video) and text inputs, while the small variants (E2B, E4B) and the 12B Unified model support audio as well.

With this prompt, the model natively responds in JSON format with the detected bounding boxes - no need for specific instructions or grammar-constrained generation. We found the coordinates refer to an image size of 1000x1000, relative to the input dimensions.

Gemma 4 comes with first-class transformers support from the get-go 🤗. This integration allows using the model with other libraries like bitsandbytes, PEFT and TRL. Make sure to install the latest version of transformers.

The shared KV cache is an efficiency optimization that reduces both compute and memory during inference. The last num_kv_shared_layers layers of the model don't compute their own key and value projections. Instead, they reuse the K and V tensors from the last non-shared layer of the same attention type (sliding or full).

In practice, this has a minimal impact on quality while being much more efficient (in terms of both memory and compute) for long context generation and on-device use.

We saw in our tests that Gemma 4 supports comprehensive multimodal capabilities out of the box. We don't know what was the training mix, but we had success using it for tasks such as OCR, speech-to-text, object detection, or pointing. It also supports text-only and multimodal function calling, reasoning, code completion and correction.