Can RTX 3090 Ti run Gemma 3 27B?
Yes — runs locally
~22 tok/sec · Good — slight pause, then text streams smoothly.
The verdict
The RTX 3090 Ti (24 GB VRAM) handles Gemma 3 27B comfortably using the Q4_K_M quantization, which fits in 15.9 GB. Expected throughput is around 22 tokens/second, which feels Good — slight pause, then text streams smoothly. in interactive use. Google's flagship open model. Near GPT-4 quality. Needs 20GB+ RAM.
Setup tutorial: Gemma 3 27B on RTX 3090 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Gemma 3 27B on an NVIDIA GeForce RTX 3090 Ti with Ollama using the Q4_K_M quantization. Expect Grade S performance at ~45 tok/sec.
Prerequisites
Before starting, ensure you have at least 15.4GB of free disk space, a 64-bit version of Windows or Linux, and the latest NVIDIA drivers (version 525.60 or later) with CUDA 11.8 installed.
Expected performance
With the Q4_K_M quantization, you can expect ~45 tok/sec performance, utilizing 15.9GB of VRAM. The remaining 8.1GB of VRAM provides ample headroom for a practical context window of up to 20,000 tokens, ensuring smooth and efficient operation.
1. Install runtimeOllama
curl -fsSL https://ollama.com/install.sh | sh
ollama config set runtime cuda2. Download the model
Download the Q4_K_M quantized version of Gemma 3 27B (15.4GB file) from Hugging Face.
ollama pull bartowski/google_gemma-3-27b-it-GGUF:google_gemma-3-27b-it-Q4_K_M.gguf3. Run it
ollama run google_gemma-3-27b-it-Q4_K_M --n-gpu-layers 27 --flash-attn
ollama chat google_gemma-3-27b-it-Q4_K_M4. Optimize for RTX 3090 Ti
For optimal performance on the NVIDIA GeForce RTX 3090 Ti with 24GB VRAM, use --n-gpu-layers 27 to offload layers to the GPU. Enable flash attention (--flash-attn) to reduce memory usage and improve speed. With 15.9GB VRAM used, you have 8.1GB of headroom for context, allowing for a practical context window of up to 20,000 tokens.
Troubleshooting
Out of memory errors during inference
Reduce the number of GPU layers with --n-gpu-layers or decrease the context length to free up VRAM.
Slow token generation
Ensure that flash attention is enabled (--flash-attn) and that the CUDA runtime is correctly configured.
Model fails to load
Verify that the model file is fully downloaded and not corrupted. Try re-downloading the model using the 'ollama pull' command.
Alternative runtimes
For users preferring different runtimes, consider LM Studio for a more user-friendly GUI, llama.cpp for low-level customization, or Jan for cloud-based deployment. Ollama is recommended for its ease of use and CUDA optimization, making it ideal for the NVIDIA GeForce RTX 3090 Ti.
Other models that run great on RTX 3090 Ti
FAQ (20)
What GPU do I need to run Gemma 3 27B?
To run Gemma 3 27B, you need a GPU with at least 15.9 GB of VRAM, such as an NVIDIA RTX 3090 or better.
Is Gemma 3 27B good for coding?
Gemma 3 27B is highly capable for coding tasks, offering near GPT-4 quality in code generation and understanding complex programming concepts.
Gemma 3 27B vs Llama 3.1 8B?
Gemma 3 27B has more parameters (27B vs 8B) and generally performs better in complex tasks, but requires significantly more VRAM and computational resources.
Can I run Gemma 3 27B on a Mac?
Yes, you can run Gemma 3 27B on a Mac, but you will need a Mac with an M1 Ultra or higher to meet the VRAM requirements.
How much VRAM does Gemma 3 27B need?
Gemma 3 27B requires at least 15.9 GB of VRAM, which can vary slightly depending on the quantization level used.
Is Gemma 3 27B censored?
Gemma 3 27B is not inherently censored, but its responses can be filtered or moderated based on the implementation and configuration settings.
Is Gemma 3 27B commercial-use allowed?
Gemma 3 27B is licensed under the 'gemma' license, which allows for commercial use, provided you comply with the terms of the license.
Gemma 3 27B context length?
Gemma 3 27B supports a context length of up to 32,768 tokens, allowing for extensive and detailed conversations.
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