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 23,433 times.
To run google/gemma-7b-it locally at a 4,096-token context, its quantized versions need between 33.11 GB (GGUF, lowest quality) and 33.11 GB (GGUF, highest quality) of memory, weights plus KV cache and a system margin included.
Available GGUF quantizations for google/gemma-7b-it include GGUF. The model supports a native context length of up to 8,192 tokens; a longer context grows the KV cache and the memory needed.
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 | 0.49 GB | 33.11 GB | — | Insufficient |
KV cache estimated (architecture unavailable). Speed is a rough estimate bounded by memory bandwidth.
Frequently asked questions
What kind of model is google/gemma-7b-it?
google/gemma-7b-it is an instruction-tuned chat model with 8.54 billion parameters, based on the gemma architecture. It is released under the gemma license and distributed as GGUF files for local inference.
How much VRAM do you need to run google/gemma-7b-it?
You need about 33.11 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 context length does google/gemma-7b-it support?
google/gemma-7b-it supports a native context length of up to 8,192 tokens. A longer context grows the KV cache, so it increases the memory needed to run the model.
What is the best quantization for google/gemma-7b-it?
For google/gemma-7b-it, 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.