Model requirements
Gemma 2 27B GPU Requirements
Gemma 2 27B usually starts around 16-20 GB in INT4, 30-36 GB in INT8, and 54-60 GB in FP16. A safe production starting point is RTX 6000 Ada 48GB or A100 80GB.
Approximate starting range before runtime headroom.
Useful for accuracy-first deployments.
A strong default when you want one safe answer fast.
Direct answer
The fast answer for Gemma 2 27B
Gemma 2 27B usually starts around 16-20 GB in INT4, 30-36 GB in INT8, and 54-60 GB in FP16. A safe production starting point is RTX 6000 Ada 48GB or A100 80GB.
Gemma 2 27B fits most cleanly when you start from VRAM, not brand names.
Gemma 2 27B usually needs about 16-20 GB in INT4, 30-36 GB in INT8, and 54-60 GB in FP16. A safe starting route is RTX 6000 Ada 48GB or A100 80GB.
For Gemma 2 27B, the route decision starts with memory fit. The model usually needs about 16-20 GB in INT4, 30-36 GB in INT8, and 54-60 GB in FP16 before you add runtime headroom.
- Safe starting GPU: RTX 6000 Ada 48GB or A100 80GB
- Best general production routes: RTX 6000 Ada 48GB, A100 80GB, H100 80GB
- Add headroom for runtime behavior instead of treating the model size as the whole answer.
VRAM table
Gemma 2 27B memory and route profile
Gemma 2 27B is primarily used for higher-quality open-model inference without jumping to the heaviest 70b class. Most teams start with the quickest safe answer for memory fit, then compare which production routes make sense.
The ranges on this page are practical starting points for planning. Actual deployment requirements still depend on runtime overhead, batching, and the execution framework.
Execution notes
What changes the route in production
A memory-fit answer is only useful if the route is healthy. Pages like this should explain that fit, latency, and route quality all matter once the model goes live.
For Gemma 2 27B, the most relevant follow-up pages are the cost page and the run-without-GPU page because those are the next practical questions most teams ask.
- Higher-quality assistants
- Internal knowledge tools
- Teams balancing quality and spend
About the author
Platform engineer, Jungle Grid
Platform engineer documenting Jungle Grid's routing, pricing, and execution workflow from inside the product and codebase.
- Maintains Jungle Grid's public landing content, product docs, and SEO content library in this repository.
- Builds across the routing, pricing, and developer-facing product surfaces that the public site describes.
Why trust this page
This content is based on current Jungle Grid product behavior, public docs, and the live pricing and routing surfaces used throughout the site.
- Gemma 2 27B route guidance here uses the current model library values stored in Jungle Grid's public landing app.
- Cost and fit explanations align with the workload-first execution flow and live estimator exposed on the pricing surface.
- This page is reviewed against the current public docs and model-route assumptions used throughout the site.
Next step
Take Gemma 2 27B from research into a real route
Once the fit is clear, price the route and test one workload so you can compare the theory against live capacity.
Related pages
Related model pages
Use the sibling pages below to compare requirements, cost, and remote execution options for this model.
FAQ
Frequently asked
What GPU do I need for Gemma 2 27B?
A safe starting answer is RTX 6000 Ada 48GB or A100 80GB. Lighter quantized routes can use less memory, but that is the clean default most teams need first.
Can Gemma 2 27B run on a consumer GPU?
In many cases yes, especially with quantization. The safer answer still depends on the exact precision, runtime overhead, and traffic shape you expect in production.
Why should this page link to pricing and run-without-GPU pages?
Because the next user question after requirements is usually either cost or whether the model can be run remotely without buying hardware directly.