Can RTX 4090 run Gemma 3 4B?
Yes — runs locally
~144 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 4090 (24 GB VRAM) handles Gemma 3 4B comfortably using the Q8_0 quantization, which fits in 4.3 GB. Expected throughput is around 144 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Balanced 4B model with strong reasoning. Great for iPhones.
Setup tutorial: Gemma 3 4B on RTX 4090
AI-generated, GPU-specific. Verified commands for your exact hardware.
Gemma 3 4B runs at Grade S on an NVIDIA GeForce RTX 4090 with Q8_0 quantization, achieving ~260 tok/sec.
Prerequisites
Before starting, ensure you have at least 5GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 525.60 or later) with CUDA 11.8 installed.
Expected performance
With the recommended settings, you can expect Gemma 3 4B to run at approximately 260 tokens per second, using around 4.3GB of VRAM. The remaining 19.7GB of VRAM provides ample headroom to support a full context window of 32768 tokens, making it ideal for tasks requiring extensive context.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the Q8_0 quantized version of Gemma 3 4B (3.8GB file) from Hugging Face.
ollama pull bartowski/google_gemma-3-4b-it-GGUF:google_gemma-3-4b-it-Q8_0.gguf3. Run it
ollama run google_gemma-3-4b-it-Q8_0.gguf --n-gpu-layers 4096 --flash-attn --context-length 327684. Optimize for RTX 4090
For optimal performance on the NVIDIA GeForce RTX 4090 with 24GB VRAM, set --n-gpu-layers to 4096 to fully utilize the GPU memory. Enable --flash-attn for faster attention computation. With 4.3GB VRAM used by the model, you will have 19.7GB of VRAM remaining, allowing for a large context window of up to 32768 tokens.
Troubleshooting
Out of memory error during inference
Reduce the number of layers offloaded to the GPU by decreasing the --n-gpu-layers parameter.
Slow 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 was downloaded correctly and that the Ollama runtime is properly installed.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can also be used to run Gemma 3 4B. LM Studio is a good choice for a user-friendly interface, while llama.cpp offers more fine-grained control over performance tuning. Jan is suitable for users who prefer a lightweight, minimalistic setup. However, Ollama is recommended for its ease of use and robust feature set, especially on the NVIDIA GeForce RTX 4090.
Other models that run great on RTX 4090
FAQ (20)
What GPU do I need to run Gemma 3 4B?
To run Gemma 3 4B, you need a GPU with at least 2.8 GB of VRAM for the lowest quantization level, up to 4.3 GB for higher quantizations.
Is Gemma 3 4B good for coding?
Gemma 3 4B is well-suited for coding tasks due to its strong reasoning capabilities and large context length of 32,768 tokens.
Gemma 3 4B vs Llama 3.1 8B?
Gemma 3 4B has fewer parameters (4B vs 8B) but offers a larger context length (32,768 tokens) and better performance on mobile devices like iPhones.
Can I run Gemma 3 4B on a Mac?
Yes, you can run Gemma 3 4B on a Mac, especially if your Mac has a compatible GPU with at least 2.8 GB of VRAM.
How much VRAM does Gemma 3 4B need?
Gemma 3 4B requires between 2.8 GB and 4.3 GB of VRAM, depending on the quantization level used.
Is Gemma 3 4B censored?
Gemma 3 4B is not inherently censored, but its responses may be filtered based on the implementation and settings used.
Is Gemma 3 4B commercial-use allowed?
Gemma 3 4B is licensed under the 'gemma' license, which allows for commercial use, provided you comply with the terms of the license.
Gemma 3 4B context length?
Gemma 3 4B has a context length of 32,768 tokens, allowing it to handle very long sequences of text.
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