Can RTX 5090 run Gemma 3 4B?
Yes — runs locally
~168 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 5090 (32 GB VRAM) handles Gemma 3 4B comfortably using the Q8_0 quantization, which fits in 4.3 GB. Expected throughput is around 168 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 5090
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Gemma 3 4B Q8_0 on an NVIDIA GeForce RTX 5090 for Grade S performance at ~346 tok/sec, using 4.3GB VRAM.
Prerequisites
Before starting, ensure you have at least 8GB of free disk space, a compatible operating system (Windows or Linux), the latest NVIDIA drivers (version 525.60.13 or later), and CUDA 11.8 or later installed.
Expected performance
With the recommended settings, you can expect ~346 tok/sec performance while using 4.3GB of VRAM, leaving 27.6GB of VRAM available for context. This allows for a practical context window of up to 32768 tokens, given the remaining VRAM.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the 3.8GB Q8_0 quantized version of Gemma 3 4B 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 32 --flash-attn --tensor-parallelism 2
ollama chat4. Optimize for RTX 5090
For optimal performance on the NVIDIA GeForce RTX 5090 with 32GB VRAM, set --n-gpu-layers to 32 to fully utilize the GPU memory. Enable --flash-attn for faster attention computation and set --tensor-parallelism to 2 to distribute the workload efficiently across the GPU cores.
Troubleshooting
Out of memory error during inference
Reduce --n-gpu-layers to 16 or 8 to lower VRAM usage.
Low token generation speed
Ensure --flash-attn is enabled and --tensor-parallelism is set to 2.
Model fails to load
Check if the model file is corrupted and re-download it using the 'ollama pull' command.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for more advanced customization or specific use cases. LM Studio offers a graphical interface and is suitable for users who prefer a GUI. llama.cpp provides more fine-grained control over quantization and is ideal for researchers. Jan is a lightweight runtime that is easy to deploy but may lack some features compared to Ollama.
Other models that run great on RTX 5090
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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