beats DeepSeek V3, OpenAI mini
And Google is back in the Generative AI race, and that too with a bang. Google just now launched Gemma3 in 4 different variants, and given the early reviews, the model looks like a monster, given the hardware required to run it is quite minimal.
https://medium.com/media/60a8086b4b221886bd526ad231265e2f/href
Key features Gemma3
1. Multimodal Processing
Gemma 3 seamlessly integrates text and vision processing, making it ideal for tasks like:
- Visual question answering
- Image-based storytelling
- Document classification
The 4B, 12B, and 27B models are particularly strong in these areas.
2. Extended Context Handling
With a 128,000-token context window, the larger Gemma 3 models can process vast amounts of information. This is a game-changer for:
- Long-form content generation
- Complex, multi-turn conversations
- In-depth document analysis
3. Multilingual Capabilities
Gemma 3 supports 35+ languages natively and can work with over 140 languages. This makes it perfect for:
- Translation tasks
- Optical Character Recognition (OCR)
- Handwriting recognition
4. Function Calling & Structured Output
With built-in support for function calling, Gemma 3 can be used for task automation and AI-driven workflows, making it highly adaptable for real-world applications.
What Makes Gemma 3 Stand Out?
1. Best-in-Class Single-Accelerator Performance
Gemma 3 outperforms competitors like Llama-405B, DeepSeek-V3, and o3-mini in preliminary human evaluations from the LMArena leaderboard. It is amongst the top 10 LLMs now, and the best Non-Reasoning LLM on the list
What is Single-Accelerator Performance?
Single-accelerator performance refers to how well an AI model performs when running on a single hardware unit — such as a single GPU (Graphics Processing Unit) or TPU (Tensor Processing Unit) — rather than needing multiple devices working together.
Why Does This Matter?
Most large AI models require multiple accelerators (like multiple GPUs) to run efficiently. However, if a model can achieve high performance on just one accelerator, it offers several advantages:
Lower Costs — Running on a single GPU is much cheaper than using multiple GPUs or TPUs.
Easier Deployment — Less complexity in setting up AI workloads.
Better Accessibility — More people can use the model without needing high-end hardware.
2. Advanced Training for Better Alignment
Trained using reinforcement learning from human feedback (RLHF) and other fine-tuning techniques, Gemma 3 aligns well with user expectations while maintaining safety.
3. Optimized for Diverse Hardware
Gemma 3 runs efficiently on:
- NVIDIA GPUs
- Google Cloud TPUs
- AMD GPUs via ROCm stack
This ensures lower deployment costs and broader accessibility.
Early Performance Insights
- The 4B model performs exceptionally well on vision-language tasks, especially document processing.
- All models are strong candidates for fine-tuning on specific tasks.
- The 27B model is highly recommended for function calling, mathematical reasoning, and code generation.
How to Use Gemma 3?
Gemma 3 is integrated with Transformers and TGI, making it easy to deploy.
The model weights are open-sourced and can be accessed below
google/gemma-3-1b-it · Hugging Face
code snippet to run the model
#update transformers before use
!pip install git+https://github.com/huggingface/transformers@v4.49.0-Gemma-3
from transformers import pipeline
pipe = pipeline("text-generation", model="google/gemma-3-1b-it", device="cuda", torch_dtype=torch.bfloat16)
messages = [
[
{
"role": "system",
"content": [{"type": "text", "text": "You are a helpful assistant."},] },
{
"role": "user",
"content": [{"type": "text", "text": "Write a poem on Hugging Face, the company"},] },
],
]
output = pipe(messages, max_new_tokens=50)
Hope you try it out !! the early reviews are pretty good
Google Gemma3: The Best Non-Reasoning LLM was originally published in Data Science in your pocket on Medium, where people are continuing the conversation by highlighting and responding to this story.
