Technical Specifications

System Requirements

VRAM requirements for different quantization methods and context sizes

Gemma 2 27B

Gemma 2 is a family of advanced, open models developed by Google DeepMind, stemming from the same research that informed the Gemini models. This model family aims to provide robust capabilities for a range of text generation tasks, including but not limited to question answering, summarization, and reasoning. The 27B variant is engineered for efficient inference, facilitating deployment across various hardware environments, from high-performance workstations to more constrained consumer devices.

The architecture of Gemma 2 represents a progression in Transformer design, integrating several key innovations. These include the adoption of Grouped-Query Attention (GQA) and a strategic interleaving of local and global attention layers. This architectural refinement contributes to enhanced performance and improved inference efficiency, particularly when processing extended contexts. Furthermore, the model employs Logit soft-capping for training stability and incorporates Rotary Position Embeddings (RoPE) for effective positional encoding. Notably, the smaller 2B and 9B models within the Gemma 2 family were developed using knowledge distillation from a larger teacher model.

The Gemma 2 27B model is designed to achieve a high level of performance within its parameter class, while prioritizing computational efficiency. This efficiency enables cost-effective deployment, as the model supports full precision inference on a single high-performance GPU or TPU. The model's capabilities are applicable to tasks requiring sophisticated natural language understanding and generation, making it suitable for applications in content creation, conversational AI systems, and fundamental natural language processing research.