gemma-2b-it GGUF size and VRAM requirements
google/gemma-2b-it is a mid-size instruction-tuned chat model with 2.51 billion parameters, built on the gemma architecture. It is released under the gemma license and has been downloaded 80,337 times.
To run google/gemma-2b-it 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-it fits entirely in the VRAM of a 12 GB GPU or larger, running fully on the GPU.
GGUF file size and memory by quantization
Compare real GGUF weight sizes, estimated KV cache and total memory for Q4, Q5, Q8 and every quantization published in this repository.
| 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-it?
You need about 10.91 GB of VRAM to run google/gemma-2b-it 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-it on an 8 GB GPU?
Partially. google/gemma-2b-it 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-it on a 16 GB GPU?
Yes. With 16 GB of VRAM you can run google/gemma-2b-it fully on the GPU using GGUF (about 10.91 GB).
Can I run google/gemma-2b-it on a 24 GB GPU?
Yes. With 24 GB of VRAM you can run google/gemma-2b-it fully on the GPU using GGUF (about 10.91 GB).
What is the best quantization for google/gemma-2b-it?
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.