Can RTX 5060 Ti run Gemma 3 27B?
Yes — runs locally
~0 tok/sec · Cannot run — model too large for this GPU
The verdict
The RTX 5060 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 5060 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Gemma 3 27B on an NVIDIA GeForce RTX 5060 Ti with Q4_K_M quantization for a Grade B experience, achieving ~30 tok/sec.
Prerequisites
Before starting, ensure you have at least 15.4GB of free disk space, a compatible OS (Windows or Linux), and the latest NVIDIA drivers (version 525.60.13 or later) with 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. The practical context window will be close to 32768 tokens, given the remaining VRAM headroom of 0.1GB.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the Q4_K_M quantized version of Gemma 3 27B (15.4GB) 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 google_gemma-3-27b-it-Q4_K_M --n-gpu-layers 27 --flash-attn --context-length 327684. Optimize for RTX 5060 Ti
For optimal performance on the NVIDIA GeForce RTX 5060 Ti with 16GB VRAM, set --n-gpu-layers to 27 to maximize GPU utilization while leaving enough VRAM for context. Enable --flash-attn for efficient attention computation. Given the 15.9GB VRAM usage, you can comfortably handle a context length of 32768 tokens with about 0.1GB of VRAM headroom.
Troubleshooting
Out of memory error during inference
Reduce the --n-gpu-layers value to 25 or lower to free up more VRAM.
Low token generation speed
Ensure that --flash-attn is enabled and that your CUDA installation is up to date.
Model fails to load
Verify that the model file is correctly downloaded and not corrupted. Try re-downloading the model.
Alternative runtimes
Consider using LM Studio for a more user-friendly interface, llama.cpp for fine-grained control over quantization and performance tuning, or Jan for a lightweight, portable runtime. Choose based on your specific needs for ease of use, customization, or portability.
Other models that run great on RTX 5060 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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