Gemma 2 27B
GemmaGoogle · 27B · Dense
Google's largest Gemma 2 model
Use Cases
| Quant | Bits | VRAM | Quality | Status |
|---|---|---|---|---|
| Q2_K | 2 | 9.1 GB | low | — |
| Q3_K_M | 3 | 12.6 GB | moderate | — |
| Q4_K_M | 4 | 14.3 GB | good | — |
| Q5_K_M | 5 | 17.8 GB | good | — |
| Q6_K | 6 | 21.2 GB | excellent | — |
| Q8_0 | 8 | 28.2 GB | excellent | — |
| F16 | 16 | 55.8 GB | lossless | — |
GPUs that run Gemma 2 27B
Gemma 2 27B needs about 14.3 GB of VRAM at Q4_K_M, so it runs comfortably on a GPU with 20 GB of VRAM or more (39 of the GPUs we track). Common cards, smallest first:
| VRAM | GPUs |
|---|---|
| 20 GB | RTX 4000 SFF Ada , RX 7900 XT , RTX 4000 Ada |
| 24 GB | RTX 4090 , RX 7900 XTX , RTX 3090 Ti |
| 32 GB | RTX 5090 , RTX 5000 Ada , Tesla V100 32GB |
About this model
Google’s Gemma 2 model is available in three sizes, 2B, 9B and 27B, featuring a brand new architecture designed for class leading performance and efficiency.
Class leading performance
At 27 billion parameters, Gemma 2 delivers performance surpassing models more than twice its size in benchmarks. This breakthrough efficiency sets a new standard in the open model landscape.
Three sizes: 2B, 9B and 27B parameters
- 2B Parameters
ollama run gemma2:2b - 9B Parameters
ollama run gemma2 - 27B Parameters
ollama run gemma2:27b
Benchmark
Intended Usage
Open Large Language Models (LLMs) have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development.
- Content Creation and Communication
- Text Generation: These models can be used to generate creative text formats such as poems, scripts, code, marketing copy, and email drafts.
- Chatbots and Conversational AI: Power conversational interfaces for customer service, virtual assistants, or interactive applications.
- Text Summarization: Generate concise summaries of a text corpus, research papers, or reports.
- Research and Education
- Natural Language Processing (NLP) Research: These models can serve as a foundation for researchers to experiment with NLP techniques, develop algorithms, and contribute to the advancement of the field.
- Language Learning Tools: Support interactive language learning experiences, aiding in grammar correction or providing writing practice.
- Knowledge Exploration: Assist researchers in exploring large bodies of text by generating summaries or answering questions about specific topics.
Using Gemma 2 with popular tooling
LangChain
from langchain_community.llms import Ollama
llm = Ollama(model="gemma2")
llm.invoke("Why is the sky blue?")
LlamaIndex
from llama_index.llms.ollama import Ollama
llm = Ollama(model="gemma2")
llm.complete("Why is the sky blue?")