Gemma 4 on vLLM: Advanced Reasoning and Multimodal Capabilities

vLLM has announced immediate support for Gemma 4, Google's most sophisticated open model lineup. This release enables developers to deploy high-intelligence-per-parameter models with advanced reasoning and agentic capabilities across a wide range of hardware backends, including Google TPUs, AMD GPUs, and Intel XPUs.

Model Family and Architecture

Gemma 4 is built using the same research and technology as Gemini 3 and is released under a commercially permissive Apache 2.0 license. The family consists of four distinct model sizes tailored for different hardware environments:

• Effective 2B (E2B): Designed for edge devices.
• Effective 4B (E4B): Designed for edge devices.
• 26B Mixture of Experts (MoE): A larger, efficient variant.
• 31B Dense: The largest dense model in the family.

Core Technical Capabilities

Gemma 4 introduces several breakthroughs in reasoning, modality, and scale to support frontier research and product innovation:

Advanced Reasoning and Agentic Workflows

Gemma 4 is engineered for complex multi-step planning and provides significant improvements in math and logic-heavy instruction-following. To support autonomous agents, the models natively support:

• Function-calling
• Structured JSON output
• System instructions

Multimodal Processing

All Gemma 4 models can natively process images and video with variable resolution, specifically excelling at OCR and chart understanding. Additionally, the edge models (E2B and E4B) include native audio input for speech recognition.

Context Window and Language Support

The models support extensive datasets through expanded context windows: 128K for edge models and up to 256K for larger variants. Furthermore, Gemma 4 was natively trained on over 140 languages, facilitating the development of global applications.

Code Generation

The lineup provides high-quality offline code support, enabling local-first AI development environments on standard workstations.

Hardware Compatibility and Deployment

vLLM provides optimized support for Gemma 4 across multiple hardware backends to reduce hardware overhead. Deployment is supported on:

• Nvidia GPUs
• AMD GPUs
• Intel XPUs
• Google TPUs

Support ranges from laptop-class hardware to datacenter accelerators, including specific integration for Google Kubernetes (GKE) and Google Compute Engine (GCE) using Trillium and Ironwood TPUs.