gemma-7b-it GGUF size and VRAM requirements
google/gemma-7b-it is a large instruction-tuned chat model with 8.54 billion parameters, built on the gemma architecture. It is released under the gemma license and has been downloaded 21,035 times.
To run google/gemma-7b-it locally at a 4,096-token context, its quantized versions need between 34.02 GB (GGUF, lowest quality) and 34.02 GB (GGUF, highest quality) of memory, weights plus KV cache and a system margin included.
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 | 32.01 | Excellent | 31.81 GB | 1.41 GB | 34.02 GB | — | Insufficient |
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-7b-it?
You need about 34.02 GB of VRAM to run google/gemma-7b-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-7b-it on an 8 GB GPU?
No. google/gemma-7b-it does not fit on an 8 GB GPU, even with the smallest quantization and system RAM offloading.
Can I run google/gemma-7b-it on a 16 GB GPU?
Partially. google/gemma-7b-it only fits on a 16 GB GPU by offloading part of it to system RAM (with GGUF), which runs but is slower.
Can I run google/gemma-7b-it on a 24 GB GPU?
Partially. google/gemma-7b-it only fits on a 24 GB GPU by offloading part of it to system RAM (with GGUF), which runs but is slower.
What is the best quantization for google/gemma-7b-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.