Who should use this model?
- higher-quality local chat
- summarization
- drafting
- workstation inference
Choose something else when
- image input is required
- less than 32GB system RAM is available
- native tool/function calling is required
What hardware makes sense?
The download is only part of the memory needed. Leave room for the app, the input it processes, and other open programs. For chat models, context includes the instructions, conversation, and answer. Longer context needs more memory.
- CPU only
- Memory or response time may limit use. Try a smaller model first.
- 8GB system memory
- Use a smaller model for this memory limit.
- 16GB system memory
- Use a smaller model for this memory limit.
- 32GB system memory
- Memory or response time may limit use. Try a smaller model first.
- 8GB GPU memory
- Use a smaller model for this memory limit.
- 12GB GPU memory
- Use a smaller model for this memory limit.
- 24GB GPU memory
- A possible starting point with a compressed model and short conversation; check the exact file size.
- Apple unified memory
- Memory or response time may limit use. Try a smaller model first.
Model facts that change the decision
- Parameters
- 27B
- Input
- Text
- Published context limit
- 8,192 tokens
- Model format
- safetensors · GGUF · Ollama · Transformers · Kaggle
- Suggested compression
- Q4_K_M for default local use; Q5_K_M if the model still fits comfortably in RAM/VRAM.
- Typical Q4 file
- 16.6GB before runtime and context overhead
- Languages
- English
First setup
Ollama
ollama run gemma2:27bLM Studio: Search for bartowski/gemma-2-27b-it-GGUF and select Q4_K_M or Q5_K_M.
llama.cpp
Use hf.co/bartowski/gemma-2-27b-it-GGUF:Q4_K_M; increase context only if memory permits.
Conditions that change the answer
- Gemma 2 is text-only; do not treat it as a vision-language model.
- GGUF files are community conversions, not official Google GGUF releases.
- Allow memory for the model file, the app, and your conversation.
Read the controlling license text →