Run Gemma 2 27B locally: hardware requirements
Gemma 2 27B by Google installs on a paired computer through idapt's local engine. RAM decides whether a build fits; a GPU only changes speed. Local runs are not billed.
- Download
- 16 GB
- RAM needed
- 24 GB
- Builds
- 1
Will it run on your machine?
Enter your system RAM to get verdicts. RAM decides whether a build fits; a GPU only changes speed.
Every build
| Build | Quantization | Download | RAM needed |
|---|---|---|---|
| 27b-instruct-q4_K_MDefault | q4_K_M | 16 GB | 24 GB |
RAM figures are working-set recommendations: when GPU memory is short, the runtime spills to CPU and system memory instead of failing.
Install it
Pair a computer with idapt and install Gemma 2 27B from the model page: the local engine bootstraps itself, pulls the build you pick, and streams progress. Or pull it directly:
ollama pull gemma2:27b-instruct-q4_K_MFrequently asked
How much RAM do I need to run Gemma 2 27B locally?
The smallest Gemma 2 27B build runs with about 24 GB of system RAM; the largest needs 24 GB. RAM is the binding requirement: when GPU memory is short, the runtime spills to CPU and system memory instead of failing.
Which quantization of Gemma 2 27B should I pick?
Start with the default 27b-instruct-q4_K_M build: a 16 GB download that wants about 24 GB of RAM. Smaller quantizations shrink the download and memory needs at some cost in output quality; larger ones do the reverse.
Do I need a GPU to run Gemma 2 27B?
No. A GPU with enough VRAM makes generation faster, but Gemma 2 27B runs on CPU and system RAM alone. Whether a build fits is decided by RAM; the GPU only changes speed.
How big is the Gemma 2 27B download?
About 16 GB for the single available build.
Can idapt install Gemma 2 27B for me?
Yes. Pair a computer with idapt and install Gemma 2 27B from this page: the local engine bootstraps itself, pulls the build you pick (ollama pull gemma2), and streams download progress. Local runs are not billed.
Part of the Gemma 2 27B model page · Gemma 2 27B pricing · Check every local model against your hardware