Can RTX 4080 SUPER run Gemma 3 1B?
Yes — runs locally
~156 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4080 SUPER (16 GB VRAM) handles Gemma 3 1B comfortably using the Q8_0 quantization, which fits in 1.5 GB. Expected throughput is around 156 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Google's latest tiny 1B model. Excellent quality for its size.
Setup tutorial: Gemma 3 1B on RTX 4080 SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Gemma 3 1B on an NVIDIA GeForce RTX 4080 SUPER with Q8_0 quantization for Grade S performance at ~637 tok/sec.
Prerequisites
Before starting, ensure you have at least 10GB of free disk space, a 64-bit version of Windows or Linux, and the latest NVIDIA drivers (version 525.60 or later) installed along with CUDA 11.8 or later.
Expected performance
With the Q8_0 quantization, you can expect ~637 tok/sec performance, using approximately 1.5GB of VRAM. The remaining 14.5GB of VRAM provides ample headroom for a practical context window of up to 32768 tokens, ensuring smooth and efficient operation.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the Q8_0 quantized version of Gemma 3 1B (1.0GB file) from Hugging Face.
ollama pull bartowski/google_gemma-3-1b-it-GGUF:google_gemma-3-1b-it-Q8_0.gguf3. Run it
ollama run google_gemma-3-1b-it-Q8_0.gguf --n-gpu-layers 32 --flash-attn
ollama chat google_gemma-3-1b-it-Q8_0.gguf4. 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. With 1.5GB VRAM used by the model, you have 14.5GB of VRAM available for context, allowing for a large practical context window of up to 32768 tokens.
Troubleshooting
Out of memory error during inference
Reduce --n-gpu-layers to 16 or enable --cpu-offload to offload some layers to the CPU.
Slow inference speed
Ensure that --flash-attn is enabled and that your CUDA drivers are up to date.
Model fails to load
Check that the model file is downloaded correctly and that the file path is correct in the run command.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for more advanced customization or different use cases. LM Studio offers a graphical interface and is suitable for users who prefer a GUI. llama.cpp is ideal for low-memory systems and offers fine-grained control over optimizations. Jan is a lightweight runtime that can be useful for quick prototyping or deployment in resource-constrained environments.
Other models that run great on RTX 4080 SUPER
FAQ (20)
What GPU do I need to run Gemma 3 1B?
To run Gemma 3 1B, you need a GPU with at least 1.3 GB to 1.5 GB of VRAM, depending on the quantization level.
Is Gemma 3 1B good for coding?
Gemma 3 1B is suitable for coding tasks due to its efficient size and high-quality outputs, making it a good choice for developers.
Gemma 3 1B vs Llama 3.1 8B?
Gemma 3 1B is smaller and requires less VRAM (1.3 GB to 1.5 GB) compared to Llama 3.1 8B (which needs more VRAM), but Llama 3.1 8B generally offers better performance for larger tasks.
Can I run Gemma 3 1B on a Mac?
Yes, you can run Gemma 3 1B on a Mac, provided your Mac has a compatible GPU with at least 1.3 GB to 1.5 GB of VRAM.
How much VRAM does Gemma 3 1B need?
Gemma 3 1B requires 1.3 GB to 1.5 GB of VRAM, depending on the quantization level used.
Is Gemma 3 1B censored?
Gemma 3 1B is not inherently censored, but its responses are guided by the training data and can be filtered or moderated as needed.
Is Gemma 3 1B commercial-use allowed?
Gemma 3 1B is licensed under the 'gemma' license, which allows for commercial use, provided you comply with the terms of the license.
Gemma 3 1B context length?
Gemma 3 1B supports a context length of 32,768 tokens, allowing for longer and more complex inputs.
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