Can RTX 3070 Ti run Gemma 3 27B?
Yes — runs locally
~0 tok/sec · Cannot run — insufficient VRAM
The verdict
The RTX 3070 Ti (8 GB VRAM) handles Gemma 3 27B comfortably using the Q4_K_M quantization, which fits in 15.9 GB. Expected throughput is around 0 tokens/second, which feels Cannot run — insufficient VRAM in interactive use. Google's flagship open model. Near GPT-4 quality. Needs 20GB+ RAM.
Setup tutorial: Gemma 3 27B on RTX 3070 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Gemma 3 27B on an NVIDIA GeForce RTX 3070 Ti with a grade D performance at ~15 tok/sec using the Q4_K_M quantization. Requires 15.9GB VRAM and 15.4GB disk space.
Prerequisites
Before starting, ensure you have at least 15.4GB of free disk space, a compatible operating system (Windows or Linux), the latest NVIDIA drivers (version 512.15 or later), and CUDA 11.2 or later installed.
Expected performance
With the specified configuration, you can expect a token generation rate of approximately 15 tok/sec, with 15.9GB VRAM in use. The practical context window will be limited due to the remaining VRAM of -7.9GB, so consider reducing the context length to fit within the available VRAM for better performance.
1. Install runtimeOllama
pip install ollama
ollama setup2. Download the model
Download the Q4_K_M quantized version of Gemma 3 27B, which is a 15.4GB file from the Hugging Face repository.
ollama pull bartowski/google_gemma-3-27b-it-GGUF:google_gemma-3-27b-it-Q4_K_M.gguf3. Run it
ollama run --model google_gemma-3-27b-it-Q4_K_M --n-gpu-layers 20 --flash-attn --tensor-parallelism 14. Optimize for RTX 3070 Ti
For optimal performance on the NVIDIA GeForce RTX 3070 Ti with 8GB VRAM, set --n-gpu-layers to 20 to maximize the use of available VRAM. Enable --flash-attn to reduce memory usage and improve speed. Given the limited VRAM, avoid tensor parallelism (--tensor-parallelism 1) to prevent out-of-memory errors.
Troubleshooting
Out of memory error during inference
Reduce the number of layers loaded onto the GPU with --n-gpu-layers 15 and decrease the context length to 16384 tokens.
Slow token generation rate
Ensure that --flash-attn is enabled and try increasing the batch size with --batch-size 16.
Model fails to load
Verify that the model file is correctly downloaded and not corrupted. Re-run the download command if necessary.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for running Gemma 3 27B. LM Studio is suitable for users who prefer a GUI interface, while llama.cpp offers more fine-grained control over performance tuning. Jan is a lightweight option for quick testing but may not support all features of the model. Choose based on your specific needs and preferences.
Other models that run great on RTX 3070 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.
Want personalized recommendations for your exact setup? Detect my hardware →