Run google/gemma-2b locally
google/gemma-2b is a mid-size language model with 2.51 billion parameters, built on the gemma architecture. It is released under the gemma license and has been downloaded 112,133 times.
To run google/gemma-2b locally at a 4,096-token context, its quantized versions need between 10.91 GB (GGUF, lowest quality) and 10.91 GB (GGUF, highest quality) of memory, weights plus KV cache and a system margin included.
For most users the best balance is GGUF, needing about 10.91 GB. That means google/gemma-2b fits entirely in the VRAM of a 12 GB GPU or larger, running fully on the GPU.
All quantizations
| Quant. | Bits | Quality | Weights | KV | Total | Speed~ | Verdict |
|---|---|---|---|---|---|---|---|
| GGUF | 32.02 | Excellent | 9.34 GB | 0.76 GB | 10.91 GB | 5.4 t/s | Offload |
KV cache estimated (architecture unavailable). Speed is a rough estimate bounded by memory bandwidth.
Frequently asked questions
How much VRAM do you need to run google/gemma-2b?
You need about 10.91 GB of VRAM to run google/gemma-2b entirely on the GPU using the GGUF quantization (at a 4,096-token context). Smaller quantizations lower the requirement at the cost of quality.
Can I run google/gemma-2b on an 8 GB GPU?
Partially. google/gemma-2b only fits on an 8 GB GPU by offloading part of it to system RAM (with GGUF), which runs but is slower.
Can I run google/gemma-2b on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run google/gemma-2b fully on the GPU using GGUF (about 10.91 GB).
Can I run google/gemma-2b on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run google/gemma-2b fully on the GPU using GGUF (about 10.91 GB).
What is the best quantization for google/gemma-2b?
If memory allows, higher bits-per-weight means better quality. A common sweet spot is a Q4_K_M or Q5_K_M quantization, which keeps most of the quality while roughly halving the memory versus 8-bit. Pick the highest quantization that still fits in your VRAM.