31B-Parameter Gemma 4 Torches Open-Source Rivals

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.

Total Cost of Ownership for an Indian Startup

A realistic spend model for a Bengaluru SaaS team running Gemma 4 Medium (13B) in production at 5 million tokens per day:

Self-hosted on AWS Mumbai (single A10G instance) costs roughly Rs 35,000-45,000 per month including EBS, NAT, and observability overhead. The same workload on DeepSeek V3.2 API runs roughly Rs 18,000-22,000 per month. Self-hosted Gemma is more expensive at this volume.

The break-even point favours self-hosting once daily tokens cross 25-30 million, or when data-residency policy forbids sending Indian customer data to a Chinese API. Below that, DeepSeek wins on cost, Claude or Gemini wins on accuracy for non-routine queries. Above it, Gemma 4 self-hosted wins on cost, privacy, and unit-economics predictability.

Indian seed-stage shops often miss the observability line. A self-hosted LLM needs prompt logging, latency dashboards, drift detection, and quota enforcement. Plan for an additional Rs 8,000-12,000 per month on Grafana Cloud, OpenTelemetry, or self-hosted Loki. Without it, your first production incident will eat a week of engineering time.

Fine-Tuning Gemma 4 for Indian Domains

Three Indian fine-tuning paths have produced production-ready results in the first 30 days of Gemma 4 availability:

First, BFSI document classification. Fine-tuning Gemma 4 Small (6B) on 50,000 labelled examples of RBI circulars, GST filings, and bank statements produces a classifier that matches GPT-5.4 accuracy at roughly 12% of inference cost. Total fine-tune cost on a single L4: Rs 8,000-12,000.

Second, code-switched Hindi-English customer support. Sarvam's base model still wins on pure Hindi, but Gemma 4 Medium fine-tuned on actual Indian SaaS support transcripts produces more natural code-switched replies. The training data quality is the bottleneck, anonymise carefully before training.

Third, agricultural advisory in Punjabi, Marathi, and Gujarati. Gemma 4 Nano (2.3B) fine-tuned on agricultural extension content runs on a mid-range Android device with 4GB RAM, viable for offline rural deployment. Battery cost is the practical limit, expect roughly 40-60 inferences per phone charge.

FAQ

Can Gemma 4 be used for commercial Indian SaaS without licence concerns? Yes. Apache 2.0 is the cleanest licence for commercial deployment. Verify your specific fine-tuned variant inherits the same licence, custom datasets may add restrictions.

Does Gemma 4 work on AWS Mumbai region for data residency? Yes. Deploy via SageMaker, EC2 with NVIDIA L4 or A10G GPUs, or Bedrock if AWS adds Gemma 4 to the Bedrock catalogue (announced but not yet live as of April 2026).

Will IndiaAI Mission subsidise Gemma 4 deployments? Compute subsidies (Rs 55/hour for H100 access) apply to all open-source models. Apply through the IndiaAI Mission portal, approval typically lands in 4-6 weeks.

How does Gemma 4 31B compare to Llama 4 70B for Indian production use? Gemma 4 31B is roughly 5-10% behind Llama 4 70B on English reasoning but ahead on multilingual including Hindi. The 2x smaller footprint makes Gemma 4 the cleaner choice for cost-sensitive deployments. Llama 4 wins on raw English accuracy where the size budget allows.

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

Related

• Gemma 4 on Ollama, local setup for India
• Run Gemma 4 on Ollama on Linux
• 40 models don't win the procurement call, the on-prem TCO flip

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