Can RTX 5070 Ti run Gemma 3 27B?
Yes — runs locally
~0 tok/sec · Cannot run — model too large for this GPU
The verdict
The RTX 5070 Ti (16 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 — model too large for this GPU in interactive use. Google's flagship open model. Near GPT-4 quality. Needs 20GB+ RAM.
Setup tutorial: Gemma 3 27B on RTX 5070 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Gemma 3 27B on an NVIDIA GeForce RTX 5070 Ti with a Grade B performance, using the Q4_K_M quantization, achieving ~30 tok/sec.
Prerequisites
Before starting, ensure you have at least 30GB of free disk space, a compatible operating system (Windows or Linux), the latest NVIDIA driver (version 525.60.13 or later), and CUDA 11.8 installed.
Expected performance
With the recommended settings, you can expect to achieve ~30 tok/sec with 15.9GB of VRAM in use. Given the remaining 0.1GB of VRAM, you can comfortably handle a context window of up to 32768 tokens, though larger contexts may require further tuning.
1. Install runtimeOllama
pip install ollama
ollama config set device cuda2. Download the model
Download the Q4_K_M quantized version of Gemma 3 27B, which is a 15.4GB file.
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 --context-length 32768 --n-gpu-layers 27 --flash-attn
ollama chat google_gemma-3-27b-it-Q4_K_M4. Optimize for RTX 5070 Ti
For optimal performance on the NVIDIA GeForce RTX 5070 Ti with 16GB VRAM, use --n-gpu-layers 27 to offload some layers to CPU memory, enabling flash attention (--flash-attn) to reduce VRAM usage and improve speed. This configuration will utilize approximately 15.9GB of VRAM, leaving about 0.1GB for context management.
Troubleshooting
Out of memory error during inference
Reduce the number of GPU layers using --n-gpu-layers 20 or lower, or enable CPU offloading with --cpu-offload
Slow token generation
Ensure that flash attention is enabled with --flash-attn and that the CUDA backend is properly configured with ollama config set device cuda
Model fails to load
Check the integrity of the downloaded model file and try re-downloading it using the same ollama pull command
Alternative runtimes
Alternative runtimes include LM Studio for a more user-friendly interface, llama.cpp for low-level customization, and Jan for web-based deployment. Use LM Studio if you prefer a graphical interface, llama.cpp if you need fine-grained control over performance settings, and Jan if you want to deploy the model as a web service.
Other models that run great on RTX 5070 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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