Can RTX 4060 Ti 16GB run Gemma 3 27B?
Yes — runs locally
~0 tok/sec · Cannot run — model too large for this GPU
The verdict
The RTX 4060 Ti 16GB (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 4060 Ti 16GB
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Gemma 3 27B on a NVIDIA GeForce RTX 4060 Ti 16GB with Q4_K_M quantization. Expect Grade B performance at ~30 tok/sec.
Prerequisites
Before starting, ensure you have at least 15.4GB of free disk space, a 64-bit version of Windows or Linux, the latest NVIDIA drivers (version 525.60.12 or later), and CUDA 11.8 or later installed.
Expected performance
With the specified setup, you can expect a token generation rate of ~30 tok/sec, with 15.9GB of VRAM in use, leaving about 0.1GB for context. This setup provides a comfortable balance between performance and context window size.
1. Install runtimeOllama
pip install ollama
ollama init2. 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 --context-length 327684. Optimize for RTX 4060 Ti 16GB
For optimal performance on the NVIDIA GeForce RTX 4060 Ti 16GB, set --n-gpu-layers to 27 to utilize the available 16GB VRAM effectively. Enable --flash-attn to reduce memory usage and improve speed. Given the 15.9GB VRAM requirement, you will have approximately 0.1GB of headroom for context, allowing for a practical context window of around 32K tokens.
Troubleshooting
Out of Memory (OOM) errors during inference
Reduce the number of GPU layers using --n-gpu-layers 20 or lower, or decrease the context length with --context-length 16384.
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 fully downloaded and not corrupted. Re-run the download command if necessary.
Alternative runtimes
For users preferring different runtimes, consider LM Studio for a more user-friendly interface, llama.cpp for fine-grained control over quantization and performance tuning, or Jan for a lightweight, easy-to-use alternative. Each runtime has its strengths, but Ollama is recommended for its simplicity and ease of use on this GPU.
Other models that run great on RTX 4060 Ti 16GB
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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