gemma-2-9b-it
Q4 GGUF download: bartowski/gemma-2-9b-it-GGUF - 6.84 GB on disk, read 2026-09-27. VRAM figures above are estimates; this is the actual file size.
Arena rating (LMArena)
Human preference wins from head-to-head chat battles
| Metric | Value |
|---|---|
| Overall arena rating | 1207.4 |
| Rank (overall) | #319 |
| Battle votes | 54,611 |
Overall rating published by LMArena on 2026-10-28 - a pairwise human-preference Elo across live chat battles; higher is better. Fetched 2026-11-07. Open-weights models compete head-to-head with proprietary ones here — no benchmark prompts, just what people pick.
Will it run on your hardware?
Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest
Need an exact figure for your context length? Use the VRAM calculator.
Run it locally
Copy-paste - running in under a minute
vllm serve google/gemma-2-9b-itNew to this? Start with Ollama · serve to many users with vLLM.
Deep dive
Notes, sources, and the full write-up
gemma-2-9b-it is a 9.2B-parameter gemma model from google. At Q4_K_M it needs roughly 5 GB of VRAM, placing it in the 8–12 GB GPU hardware tier.
PopularityLink to this section
gemma-2-9b-it has 864,175 downloads in the last month on HuggingFace and 954 likes.
Frequently asked
Quick answers to common questions
How much VRAM does gemma-2-9b-it need?
gemma-2-9b-it with 9.2B parameters needs approximately 5 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.
Is gemma-2-9b-it better than other google models?
gemma-2-9b-it has 9.2B parameters with 8,192 context - a strong choice for general use.
What license is gemma-2-9b-it under?
gemma-2-9b-it is released under the gemma license, making it suitable for most commercial and personal projects.
What hardware runs gemma-2-9b-it well?
With 9.2B parameters, gemma-2-9b-it requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.
What is the best quantization for gemma-2-9b-it?
Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~7 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.
What models compete with gemma-2-9b-it?
gemma-2-9b-it competes with other models in its class. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.
Nearby options
Similar models and compatible hardware by spec