Can I run gemma-2-9b-it locally?
google/gemma-2-9b-itgemma-2-9b-it is a llm (text generation) model with about 9.2B parameters. The practical minimum to run it is roughly 5.7 GB of GPU or system memory, and a GPU with ≥ 8 GB VRAM runs it comfortably. Here's exactly what it needs and how to run it.
Check YOUR exact machinegemma-2-9b-it memory & VRAM requirements
How much memory gemma-2-9b-it needs at each quantization level (weights plus ~20% runtime overhead). Lower-bit quantization dramatically reduces VRAM at a small quality cost.
| Precision | Memory needed | Notes |
|---|---|---|
| Full precision (FP16/BF16) | 23 GB | Full quality |
| 8-bit (INT8 / Q8) | 12 GB | Smaller, slight quality loss |
| 4-bit (Q4_K_M / INT4) | 7.1 GB | Best size/quality trade-off |
| 3-bit (Q3_K) | 5.7 GB | Smaller, slight quality loss |
Will gemma-2-9b-it run on your GPU?
Verdicts for common setups — from CPU-only laptops to an RTX 4090 and data-center GPUs. Click a GPU to see everything it can run.
| Your setup | Verdict | Needs | Est. speed | Best way to run |
|---|---|---|---|---|
| No GPU (16 GB RAM) | Workarounds | 12 GB | ~4.5 tok/s | Run on CPU (RAM) with Q8 |
| RTX 4060 (8 GB) | Runs | 7.1 GB | ~35 tok/s | Run on your GPU with Q4 |
| RTX 3060 (12 GB) | Runs | 7.1 GB | ~45 tok/s | Run on your GPU with Q4 |
| RTX 4070 Super (12 GB) | Runs | 7.1 GB | ~61 tok/s | Run on your GPU with Q4 |
| RTX 4080 Super (16 GB) | Runs | 12 GB | ~50 tok/s | Run on your GPU with Q8 |
| RTX 4090 (24 GB) | Runs | 12 GB | ~66 tok/s | Run on your GPU with Q8 |
| RTX 3090 (24 GB) | Runs | 12 GB | ~62 tok/s | Run on your GPU with Q8 |
| Apple M-series (18 GB unified) | Runs | 12 GB | ~11 tok/s | Run on your Apple Silicon (unified memory) with Q8 |
| Apple M Max (64 GB unified) | Runs | 23 GB | ~15 tok/s | Run on your Apple Silicon (unified memory) with FP16 |
| A100 (80 GB) | Runs | 23 GB | ~67 tok/s | Run on your GPU with FP16 |
How to run gemma-2-9b-it locally
Recommended method: Run on your GPU with FP16 using vLLM or Transformers.
# Fast production serving on http://localhost:8000/v1
vllm serve google/gemma-2-9b-itfrom transformers import pipeline
pipe = pipeline("text-generation", model="google/gemma-2-9b-it", device_map="auto")
print(pipe("Hello", max_new_tokens=50))gemma-2-9b-it — frequently asked questions
How much VRAM does gemma-2-9b-it need?
gemma-2-9b-it needs roughly 7.1 GB of VRAM at 4-bit (Q4) quantization and about 23 GB at full FP16 precision, including runtime overhead. Lower-bit quantization trades a little quality for a lot less memory.
Can I run gemma-2-9b-it on CPU without a GPU?
Yes — gemma-2-9b-it can run on CPU using system RAM (best with a quantized GGUF build via llama.cpp or Ollama), but generation will be noticeably slower than on a GPU.
What's the best way to run gemma-2-9b-it locally?
The easiest path is usually vLLM or Transformers. Run on your GPU with FP16. This page's "How to run it" section has copy-paste commands.
What GPU do I need to run gemma-2-9b-it?
A GPU with at least 8 GB of VRAM runs gemma-2-9b-it comfortably at 4-bit quantization, or about 24 GB for full FP16 precision. On Apple Silicon, unified memory of that size works too.