Trained on 6 trillion tokens, three times the data volume of its 2B sibling, the 7B Gemma model switches from multi-query to standard multi-head attention and outperforms Llama 2 13B on MMLU despite being roughly half the size. Google optimized each model in the Gemma family with distinct architectural decisions rather than simply scaling a single design up or down.
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Gemma 7B is an open-weight language model from Google DeepMind, built using the same research and technology behind the Gemini models. It is available as both a base and instruction-tuned variant for text generation, question answering, and summarization tasks.
Gemma 2B is designed for on-device and resource-constrained environments, while the 7B model offers stronger reasoning and generation quality at higher compute cost. Both share the same architecture from Google DeepMind but the 7B variant performs significantly better on benchmarks requiring complex language understanding.
Gemma 7B IT scores 64.3% on MMLU (5-shot), outperforming Llama 2 13B Chat (54.8%) despite being nearly half the size. Trained on 6 trillion tokens using Google's proprietary data pipelines, it achieves the highest MMLU score among 7B-class models on the sheet, though it trails Llama 3 8B Instruct (67.4%) in the 8B class by about 3 points.
The cost of running Gemma 7B IT with Telnyx Inference is $0.0002 per 1,000 tokens. Analyzing 1,000,000 customer chats at 1,000 tokens each would cost $200, matching the price of Mistral 7B Instruct and Llama 3 8B Instruct on the same sheet.
Gemma 7B outperforms Llama 2 7B and Mistral 7B on several standard benchmarks at launch, particularly on reasoning and knowledge tasks. It benefits from Google's training infrastructure and data curation, though newer models in the Gemma 2 and 3 series have since surpassed it.
Gemma 7B requires approximately 16GB of RAM for full-precision inference, or 8GB when using 4-bit quantization formats. This makes it runnable on consumer GPUs and accessible for local development workflows.
Gemma models are used for text generation, conversational AI, code assistance, and document summarization. The instruction-tuned variant (gemma-7b-it) is specifically designed for interactive applications where following user instructions accurately is important.
Yes, Gemma models are released under Google's permissive terms of use, allowing free access for research and commercial applications. Weights are available on Hugging Face and through various hosted inference providers.
Gemma 3 represents a significant upgrade over the original Gemma 7B, with improved reasoning and multimodal capabilities. Direct comparison with DeepSeek depends on the specific model variant and task, but Gemma 3's larger parameter options generally compete well on standard benchmarks.
