Google DeepMind has released the newest open AI model, Gemma 3, building upon the Gemini technical architecture with significant improvements in performance, context window length, and multimodal support. Described as "the best single-accelerator model in the world," Gemma 3 surpasses competitors such as Llama 3 and DeepSeek-V3 in the LMArena rankings. It also supports over 140 languages and provides a 128k token context window. This article will summarize Gemma 3's technical breakthroughs, performance comparisons, and usage.
What are the new highlights of Gemma 3? Which versions are available?
Google DeepMind Gemma 3 is the latest generation of lightweight open AI models. Compared to its predecessor, Gemma 2, it boasts multiple technical breakthroughs, including an increase in the context window length to 128k tokens, multimodal support (for versions 4B and above), and multilingual capability expanded to over 140 languages. Additionally, four different scale versions are offered. Below is an overview of Gemma 3's versions and key highlights.
Which versions does Gemma 3 offer?
Gemma 3 comes in multiple specifications, ranging from lightweight to high-performance models for different application needs. Gemma 3 adds the 1B, 4B, 12B, and 27B versions, all optimized for single GPU, TPU, and multimodal processing, allowing developers to select the most suitable model.
- 1B Version: Suited for single-GPU operation, offers a 32k token context window, lightweight and efficient.
- 4B Version: Supports multimodality (image + text), boosting AI image comprehension.
- 12B Version: For more efficient inference and applications, extended to 128k tokens.
- 27B Version: Largest in scale, outperforms Llama 3 and DeepSeek-V3, ranking near the top in the LMArena leaderboard.
How does Gemma 3 achieve its model upgrade? Five key highlights
To make Gemma 3 smarter and more accurate, Google DeepMind employed three techniques in its training: distillation, reinforcement learning, and model merging. These methods enable the model to better understand mathematics, write programs, and align more closely with human thinking.
Gemma 3 uses a more powerful AI model to extract the most crucial knowledge. Then, Google leverages human-AI interaction and applies Reinforcement Learning from Human Feedback (RLHF) to refine responses and align them with user expectations. Additionally, to enhance Gemma 3's math-solving capabilities, Google used Reinforcement Learning from Machine Feedback (RLMF). Furthermore, for more accurate programming results, Gemma 3 was trained using Reinforcement Learning from Execution Feedback (RLEF), ensuring more reliable code generation. Beyond these, Gemma 3 also features:
1. 128k Token Ultra-Long Context Window, Far Exceeding Gemma 2's 8k Limit
Gemma 3 significantly expands the context window length. The 1B version supports 32k tokens, while other versions go up to 128k tokens, several times higher than Gemma 2's 8k. This enables the AI to handle much longer contextual information, improving long-form text comprehension and complex problem-solving.
2. First-Ever Multimodal Capability, Supporting Image and Text Integration
Except for the 1B version, Gemma 3's 4B, 12B, and 27B versions all support multimodal input, enabling simultaneous understanding of images and text with text-based outputs. Its built-in SigLIP visual encoder, combined with an adaptive window algorithm, allows the model to handle high-resolution and non-square images, supporting applications like image analysis, OCR, and image Q&A.
3. New Tokenizer and Multilingual Support, Expanded to 140+ Languages
Gemma 3 natively supports over 35 languages, and with its improved tokenizer, it can be extended to 140+ languages. Google emphasizes that this helps developers build more global AI applications, enabling the AI to better understand expressions in different languages and enhancing translation and cross-lingual interactions.
4. Optimized for Single-Accelerator Operation on GPUs and TPUs, Supporting NVIDIA & AMD
Google highlights that Gemma 3 is "the best single-accelerator model in the world," specially optimized to run on a single GPU or TPU. This allows developers to build high-performance AI applications on diverse hardware, including NVIDIA GPUs, Google Cloud TPUs, and AMD GPUs.
5. Supports Function Calling and Structured Output, Enhancing AI Agent Intelligence
Gemma 3 includes built-in Function Calling and Structured Output features, allowing the AI to interact with applications more accurately. This is ideal for automated workflows, intelligent customer service, and AI Agent development, significantly boosting the AI's usefulness and integration capabilities in enterprise applications.
Gemma 3 Performance Comparison: How Does It Differ From Llama 3 and DeepSeek-V3?
As demand grows for lightweight and efficient AI models like DeepSeek, Google DeepMind introduced Gemma 3, specifically optimized for single-accelerator (GPU/TPU) use to ensure outstanding performance at low cost. In the LMArena leaderboard tests, Gemma 3 (27B version) surpasses Llama 3, DeepSeek-V3, and o3-mini, showcasing its strong competitiveness. Below is a brief comparison of Gemma 3 with Llama 3 and DeepSeek-V3.
On the LMArena leaderboard, Gemma 3 27B leads with an Elo score of 1338, outpacing Llama 3 (1269) and DeepSeek-V3 (1318) and demonstrating its excellent performance. Crucially, Gemma 3 27B can run on a single NVIDIA H100 GPU, whereas Llama 3 and DeepSeek-V3 require multiple GPUs, giving Gemma 3 a significant efficiency edge. Although the proprietary model o3-mini doesn't require a GPU, its Elo score of 1304 is still lower than Gemma 3. Overall, Gemma 3 27B offers the best price-performance ratio among these high-efficiency AI models, thanks to its single-GPU advantage.
Comparison Between Gemma 3 and Gemma 2
Many developers wonder how Gemma 3 improves upon Gemma 2. This upgrade not only covers a larger parameter range (1B–27B), a 128k token context length, and multimodal support, but also includes numerous enhancements. Below is a table summarizing the differences in these upgrades.
| Comparison Item | Gemma 3 | Gemma 2 |
|---|---|---|
| Release Date | March 2025 | June 2024 |
| Parameter Scale | 1B, 4B, 12B, 27B | 2B, 7B |
| Maximum Context Length (Tokens) | 128k (27B version) / 32k (1B version) | 8k |
| Multimodal Support | Multimodal supported (image + text input for versions 4B and above) | No multimodal support (text only) |
| Visual Encoder | SigLIP + Adaptive Window Algorithm | None |
| Performance | Leads Llama 3 and DeepSeek-V3 on the LM Arena leaderboard | Below Llama 3 and Mistral |
| Model Optimization | Deeply optimized for NVIDIA GPUs, TPUs, and AMD GPUs | Primarily optimized for NVIDIA GPUs |
| Function Calling and Structured Output | Built-in function calling to enhance AI agent applications | No built-in function calling feature |
| Language Support | Over 140 languages, with 35+ languages supported by default | Over 30 languages |
| Open Download Platforms | Hugging Face, Google AI Studio, Kaggle | Hugging Face, Google AI Studio |
Additionally, Google has compiled differences between Gemma 3 and Gemma 2 across several benchmark tests including MMLU-Pro, LiveCodeBench, SimpleQA, GPQA Diamond, and MATH. A detailed comparison is provided below:
Code Generation (LiveCodeBench)
- Gemma 3 27B：29.7
- Gemma 2 27B：20.4
Gemma 3's code generation capability has increased by 45.6%, exhibiting more stable performance on platforms like LeetCode and Codeforces. This indicates greater utility in AI coding assistants and programming support applications.
Short Q&A (SimpleQA)
- Gemma 3 27B：10.0
- Gemma 2 27B：9.2
Though Gemma 3 and Gemma 2 are quite close, Gemma 3 still has a slight edge, reflecting improved precision in providing concise answers.
Scientific Reasoning (GPQA Diamond)
- Gemma 3 27B：42.4
- Gemma 2 27B：34.3
In PhD-level tests for biology, physics, and chemistry, Gemma 3 shows a 23.6% improvement, demonstrating stronger academic reasoning and advanced problem-solving abilities.
Mathematical Reasoning (MATH)
- Gemma 3 27B：89.0
- Gemma 2 27B：55.6
Gemma 3's math-solving capability increases by 60%, indicating progress in logical reasoning, multi-step calculations, and handling mathematical formulas. This makes it suitable for financial analysis, data science, and AI-assisted mathematics.
Knowledge Understanding and Reasoning (MMLU-Pro)
- Gemma 3 27B：67.5
- Gemma 2 27B：56.9
In the MMLU-Pro tests, Gemma 3's performance is about 18.6% better than Gemma 2, reflecting improvements in its pre-training data and model architecture, resulting in superior performance in comprehensive knowledge tests and logical reasoning.
How to Use Gemma 3? Free Downloads on Hugging Face, Google AI Studio!
Want to experience the powerful AI capabilities of Gemma 3? Google has made it available on Hugging Face, Google AI Studio, and Kaggle, supporting development frameworks such as PyTorch, JAX, and Keras, with optimizations for NVIDIA GPUs, TPUs, and AMD GPUs. Whether for chatbots, code generation, or image recognition, Gemma 3 can be easily deployed. Below is an overview of how to download and use Gemma 3, helping you get started quickly.
1. Google AI Studio: Quick Trial, No Download Required
For newcomers or developers wanting a quick test of Gemma 3's AI capabilities, Google AI Studio is the most convenient choice. This free platform allows users to input text or upload images directly to test Gemma 3's multimodal processing, with no additional setup needed.
- Who it's for： Users who want to try Gemma 3 without installing it locally.
- Link： Google AI Studio
2. Download Model Weights from Hugging Face or Kaggle for Fine-Tuning
Gemma 3's freely available model weights can be downloaded from Hugging Face or Kaggle, enabling further fine-tuning. Developers can train the model for specific domains (e.g., healthcare, law, finance) to increase accuracy for specialized tasks.
- Who it's for：Developers with AI training experience who want to fine-tune the model to fit specific application needs.
- Link： Hugging Face、Kaggle
Conclusion
The success of low-cost AI models such as DeepSeek demonstrates a massive market demand for solutions with low hardware requirements but strong performance. Gemma 3's launch is Google's proactive response to this trend. Touted as "the best single-accelerator model in the world," it not only outperforms Llama-405B, DeepSeek-V3, and o3-mini on the LMArena leaderboard, but the Gemma model's over 100 million downloads also attest to the market's recognition of Google's open-model series.
Additionally, Gemma 3 can be conveniently downloaded via Hugging Face, Kaggle, and Google AI Studio, and is compatible with popular tools like Hugging Face Transformers, JAX, and PyTorch, lowering the barrier to entry and accelerating its adoption within the developer community.
As a Google Cloud technology partner, iKala has an in-depth understanding of best practices for implementing Gemma 3 in enterprise AI settings. iKala provides professional technical consulting to help businesses deploy Gemma 3 through services such as Google Cloud TPU, Vertex AI, and Cloud Run. For more information on optimizing Gemma 3 deployments on Google Cloud, feel free to contact iKala for detailed technical guidance and solutions!
