text model · gemma · Windows
Can I run Gemma 2 9B on AMD Radeon RX 7900 XTX (24GB)?
Yes. Gemma 2 9B runs on AMD Radeon RX 7900 XTX (24GB) at Q4_K_M (~7.3 GB of ~23 GB usable).
Runs at Q4_K_M using ~7.3 GB of ~23 GB usable. You have room for FP16 for higher quality.
- Q4_K_M needed
- ~7.3 GB
- Usable on device
- ~23 GB
- Device memory
- 24 GB
- Best quant
- Q4_K_M
Run it
Pick your tool. All three load the same Q4_K_M weights.
ollama run gemma2:9b llama-cli -hf bartowski/gemma-2-9b-it-GGUF:Q4_K_M lms get bartowski/gemma-2-9b-it-GGUF AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.
- Parameters
- 9B
- Q4_K_M size
- 5.76 GB
- Q8_0 size
- 9.83 GB
- Context
- 8k
- Ollama tag
- gemma2:9b
- Memory
- 24 GB vram
- Usable for weights
- ~23 GB
- Best runtime
- Ollama (ROCm) / llama.cpp ROCm (Linux)
You could also run
Run Gemma 2 9B on other hardware
FAQ
Can AMD Radeon RX 7900 XTX (24GB) run Gemma 2 9B?
Yes. Gemma 2 9B runs on AMD Radeon RX 7900 XTX (24GB) at Q4_K_M (~7.3 GB of ~23 GB usable).
How much memory does Gemma 2 9B need?
AMD Radeon RX 7900 XTX (24GB) has room to spare. At Q4_K_M the weights are ~5.76 GB; with KV cache and runtime overhead, budget ~7.3 GB at a 4k context.
What is the best tool to run Gemma 2 9B on Windows?
LM Studio for a simple setup; Ollama (CUDA) for the most speed. AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.
Sources
Memory figures are estimates. See methodology.