Can RTX 4070 Ti SUPER run Gemma 3 27B?
Yes — runs locally
~0 tok/sec · Cannot run — model too large for this GPU
The verdict
The RTX 4070 Ti SUPER (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 4070 Ti SUPER
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Gemma 3 27B on an NVIDIA GeForce RTX 4070 Ti SUPER with a B-grade performance, using the Q4_K_M quantization, achieving ~30 tokens/sec.
Prerequisites
Before starting, ensure you have at least 20GB of free disk space, a compatible operating system (Windows 10/11 or Linux), the latest NVIDIA driver (version 525.60.12 or later), and CUDA 11.8 installed.
Expected performance
With the Q4_K_M quantization, you can expect a token generation speed of approximately 30 tokens/sec, with 15.9GB of VRAM in use, leaving about 0.1GB of headroom for the context. Given the remaining VRAM, you should be able to achieve a practical context window of around 32K tokens without significant performance degradation.
1. Install runtimeOllama
pip install ollama
ollama init2. 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 --model google_gemma-3-27b-it-Q4_K_M --context-length 32768 --n-gpu-layers 32 --flash-attn
ollama chat --model google_gemma-3-27b-it-Q4_K_M4. Optimize for RTX 4070 Ti SUPER
For optimal performance on the NVIDIA GeForce RTX 4070 Ti SUPER with 16GB VRAM, use --n-gpu-layers 32 to offload some layers to CPU memory, enable --flash-attn for efficient attention computation, and consider using tensor parallelism if you have multiple GPUs. This configuration will help maintain the ~30 tok/sec throughput 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, or decrease the context length with --context-length 16384.
Slow token generation speed
Ensure that --flash-attn is enabled and that your CUDA installation is up to date. Consider increasing the batch size if your application allows it.
Model fails to load
Verify that the model file is correctly downloaded and not corrupted. Try re-downloading the model using the 'ollama pull' command.
Alternative runtimes
For users preferring different runtimes, consider LM Studio for a more user-friendly interface, llama.cpp for low-level customization, or Jan for distributed training. However, Ollama is recommended for its ease of use and performance optimization on the NVIDIA GeForce RTX 4070 Ti SUPER.
Other models that run great on RTX 4070 Ti SUPER
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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