Can RTX 5080 run Gemma 3 27B?
Yes — runs locally
~0 tok/sec · Cannot run — model too large for this GPU
The verdict
The RTX 5080 (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 5080
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Gemma 3 27B on an NVIDIA GeForce RTX 5080 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 compatible operating system (Windows or Linux), and the latest NVIDIA drivers installed (version 525.60.13 or later). Additionally, install CUDA 11.8 or later to leverage the GPU acceleration.
Expected performance
With the specified configuration, you can expect a token generation rate of approximately 30 tokens/second, with 15.9GB of VRAM in use, leaving about 0.1GB of headroom for context. Given the remaining VRAM, you can comfortably handle a practical context window of up to 32,768 tokens.
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 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 true --tensor-parallelism 14. Optimize for RTX 5080
For optimal performance on the NVIDIA GeForce RTX 5080 with 16GB VRAM, use --n-gpu-layers 27 to load most layers onto the GPU. Enable --flash-attn for faster attention computation and set --tensor-parallelism 1 to avoid splitting the model across multiple GPUs. This configuration will allow you to achieve the target ~30 tokens/second while keeping VRAM usage within the 16GB limit.
Troubleshooting
Out of memory errors during inference
Reduce the number of GPU layers with --n-gpu-layers 24 or lower.
Slow token generation rate
Ensure that CUDA is properly installed and configured. Check your GPU driver version and update if necessary.
Model fails to load
Verify the integrity of the downloaded model file and try re-downloading it.
Alternative runtimes
If you prefer a different runtime, consider LM Studio for a more user-friendly interface, llama.cpp for better CPU fallback, or Jan for advanced customization options. Use these alternatives if you need features not supported by Ollama, such as specific model optimizations or additional quantization options.
Other models that run great on RTX 5080
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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