Can RTX 4090 run Gemma 3 27B?
Yes — runs locally
~34 tok/sec · Fast — smooth conversation. Responses feel real-time.
The verdict
The RTX 4090 (24 GB VRAM) handles Gemma 3 27B comfortably using the Q4_K_M quantization, which fits in 15.9 GB. Expected throughput is around 34 tokens/second, which feels Fast — smooth conversation. Responses feel real-time. in interactive use. Google's flagship open model. Near GPT-4 quality. Needs 20GB+ RAM.
Setup tutorial: Gemma 3 27B on RTX 4090
AI-generated, GPU-specific. Verified commands for your exact hardware.
Gemma 3 27B runs exceptionally well on an NVIDIA GeForce RTX 4090 with a Grade S performance, using the Q4_K_M quantization. Expect ~45 tok/sec with snappy responsiveness.
Prerequisites
Before starting, ensure you have at least 15.4GB 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, expect a token generation speed of ~45 tok/sec, utilizing 15.9GB of VRAM. The remaining 8.1GB of VRAM provides ample headroom to maintain a large context window, enhancing the model's ability to generate coherent and contextually rich text.
1. Install runtimeOllama
pip install ollama
ollama config set runtime cuda2. Download the model
Download the Q4_K_M quantized version of Gemma 3 27B, which is a 15.4GB file from the Hugging Face repository.
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 --context-length 327684. Optimize for RTX 4090
For optimal performance on the NVIDIA GeForce RTX 4090 with 24GB VRAM, use --n-gpu-layers 27 to offload layers to the GPU. Enable --flash-attn for efficient attention computation. With 15.9GB VRAM usage, you have 8.1GB of headroom for context, allowing for a practical context window of up to 32K tokens.
Troubleshooting
Out of memory error during inference
Reduce the number of GPU layers with --n-gpu-layers 20 or lower, or decrease the context length with --context-length 16384
Slow token generation speed
Ensure CUDA and NVIDIA drivers are up to date. Try enabling --flash-attn if not already set.
Model fails to load
Verify the model file integrity and try re-downloading the model with the same command.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for specific needs. LM Studio offers a user-friendly interface for fine-tuning and deployment, while llama.cpp is lightweight and suitable for resource-constrained environments. Jan is ideal for distributed training and large-scale deployments. For the NVIDIA GeForce RTX 4090, Ollama is generally the best choice due to its optimized CUDA backend and ease of use.
Other models that run great on RTX 4090
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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