Google Ships Gemma 4 Open Source — 31B Model Lands at #3 Among Open LLMs

Google has launched Gemma 4, the latest generation of their open-source AI model family. Four variants (2.3B, 6B, 13B, 31B parameters) are available under Apache 2.0 — fully open for commercial use. The 31B Dense variant currently ranks #3 globally among open-source models on the Arena AI leaderboard.

The Four Variants

Gemma 4 Nano (2.3B): Optimized for on-device deployment. Runs on modern smartphones (Android, iPhone 15+), laptops, and edge devices.

Gemma 4 Small (6B): Workstation and server deployment. Runs on consumer GPUs (NVIDIA RTX 4060+).

Gemma 4 Medium (13B): Professional workstation tier. Requires ~16GB VRAM for full precision, ~8GB for 4-bit quantization.

Gemma 4 Large (31B Dense): The flagship. Matches or beats Llama 3.3 70B on most benchmarks while being less than half the size. Requires ~40GB VRAM full precision, runs quantized on 24GB GPUs.

All four are natively multimodal — they understand text, images, and audio out of the box without separate modality encoders.

Why Apache 2.0 Matters

Gemma 4's license is significant. Apache 2.0 permits:

• Unrestricted commercial use — no revenue thresholds or usage limits
• Modification and redistribution — fine-tune and share your variants
• Patent grant — protection from Google patent assertions on the model
• No share-alike requirement — derivative work licensing is your choice

Compare to Llama 4's custom license (which has restrictions) or closed models like GPT-5.4 (API-only access). Apache 2.0 is the most permissive option for Indian startups building products.

India-Relevant Performance

Independent evaluation on Indian language tasks shows Gemma 4 31B Dense performs competitively:

• Hindi: 81% on Sarvam-Eval (vs Sarvam-30B at 87%, GPT-5.4 at 73%)
• Tamil, Telugu, Bengali: Respectable mid-70s scores
• Code generation: 78.4% on HumanEval (vs Llama 4 Scout at 82%, GPT-5.4 at 89%)
• Multilingual reasoning: Strong on English-Hindi code-mixing, weaker on pure Tamil/Telugu reasoning

For Indian-context applications, Sarvam AI's models remain the top choice for Indic languages. Gemma 4 is the best open-source choice for general-purpose English + multilingual applications.

Practical Deployment for Indian Teams

For startups: Gemma 4 Medium (13B) runs on a single NVIDIA L4 GPU — available at ~Rs 15/hour on IndiaAI Mission subsidized compute or roughly Rs 50/hour on AWS/GCP India. Economically viable for production deployment of Indian-scale applications.

For researchers: Full fine-tuning of Gemma 4 31B on 4x H100 is feasible. With IndiaAI Mission GPU access at Rs 55/hour, full fine-tuning of 31B on 1M examples costs roughly Rs 50,000.

For on-device applications: Gemma 4 Nano (2.3B) runs on recent Android phones. Indian apps can deploy offline-first AI features without API dependencies.

Gemma 4 vs Competition

Model	Size	License	Best For
Gemma 4 31B Dense	31B	Apache 2.0	General purpose, commercial deployment
Llama 4 Scout	17B active (109B MoE)	Llama 4 Community	Massive context (10M tokens)
DeepSeek V3.2	671B MoE	MIT	Best price/performance via API
Sarvam-30B	30B	Apache 2.0	Indian language tasks
GLM-5.1	744B MoE	Custom	Best open model for coding

For most Indian open-source AI projects, the choice is Gemma 4 Medium/Large (ease of deployment + Apache 2.0) vs Sarvam-30B (Indian language focus).

Getting Started

Gemma 4 models are available on:

• Hugging Face: huggingface.co/google/gemma-4 (all four variants)
• Kaggle: Gemma 4 notebooks with free GPU access
• Google Cloud Vertex AI: Managed deployment
• Ollama: Single-command local install

For pure API access without managing infrastructure, use Replicate or Hugging Face Inference Endpoints.

Explore more open-source options in our Code & Development AI tools category.

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Source: What LLM? blog, Fazm AI blog (April 2026)