Can RTX 4080 SUPER run Gemma 3 27B?
Yes — runs locally
~0 tok/sec · Cannot run — model too large for this GPU
The verdict
The RTX 4080 SUPER (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 4080 SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Gemma 3 27B on an NVIDIA GeForce RTX 4080 SUPER with a B grade performance, using the Q4_K_M quantization. Expect ~30 tokens/second with 15.9GB VRAM usage.
Prerequisites
Before starting, ensure you have at least 20GB of free disk space, a 64-bit version of Windows or Linux, the latest NVIDIA drivers (version 525.60.11 or later), and CUDA 11.8 or later installed.
Expected performance
With the recommended settings, you can expect the model to run at approximately 30 tokens/second, utilizing 15.9GB of VRAM. The remaining 0.1GB of VRAM provides enough headroom to handle the 32K token context window comfortably.
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 --model google_gemma-3-27b-it-Q4_K_M --n-gpu-layers 32 --flash-attn --context-length 32768
ollama chat4. Optimize for RTX 4080 SUPER
For optimal performance on the NVIDIA GeForce RTX 4080 SUPER with 16GB VRAM, set --n-gpu-layers to 32 to utilize the GPU efficiently. Enable --flash-attn for faster inference and better memory management. Given the 15.9GB VRAM requirement, you will have about 0.1GB of VRAM left for context, which should allow for a practical context window of around 32K tokens.
Troubleshooting
Out of memory error during inference
Reduce the --n-gpu-layers value to 24 or 16 to lower VRAM usage.
Slow inference speed
Ensure --flash-attn is enabled and try increasing the --n-gpu-layers value to 48 if your VRAM allows it.
Inference crashes with a segmentation fault
Update your NVIDIA drivers to the latest version and reinstall CUDA.
Alternative runtimes
Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio offers a more user-friendly interface and is suitable for those who prefer a graphical environment. llama.cpp is ideal for users who need more control over the command-line and customization options. Jan is a lightweight runtime that can be used for quick prototyping and testing on this GPU.
Other models that run great on RTX 4080 SUPER
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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