Can RTX 5070 Ti run Mistral Nemo Base 12B?
Yes — runs locally
~48 tok/sec · Fast — smooth conversation. Responses feel real-time.
The verdict
The RTX 5070 Ti (16 GB VRAM) handles Mistral Nemo Base 12B comfortably using the Q4_K_M quantization, which fits in 7.7 GB. Expected throughput is around 48 tokens/second, which feels Fast — smooth conversation. Responses feel real-time. in interactive use. Official Mistral-Nemo 12B foundation model (NVIDIA collab) — pretrained only, no instruct or refusal layer. Naturally uncensored, Apache 2.0, 128K context.
Setup tutorial: Mistral Nemo Base 12B on RTX 5070 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
The Mistral Nemo Base 12B model runs exceptionally well on the NVIDIA GeForce RTX 5070 Ti with a grade S performance, using the Q4_K_M quantization, achieving ~77 tok/sec.
Prerequisites
Before starting, ensure you have at least 10GB of free disk space, a 64-bit version of Windows or Linux, the latest NVIDIA drivers (version 525.60.13 or later), and CUDA 11.8 or later installed.
Expected performance
With the Q4_K_M quantization, you can expect the model to achieve approximately 77 tokens per second, utilizing around 7.7GB of VRAM. This leaves about 8.3GB of VRAM for context, allowing for a practical context window of up to 128K tokens, which is the maximum supported by the model.
1. Install runtimeOllama
pip install ollama
ollama config set cuda_path /usr/local/cuda2. Download the model
Download the Q4_K_M quantized version of the Mistral Nemo Base 12B model (7.2GB file) from the Hugging Face repository.
ollama pull bartowski/Mistral-Nemo-Base-2407-GGUF:Mistral-Nemo-Base-2407-Q4_K_M.gguf3. Run it
ollama run Mistral-Nemo-Base-2407-Q4_K_M --context-length 131072 --n-gpu-layers 12 --flash-attn --tensor-parallelism 14. Optimize for RTX 5070 Ti
For optimal performance on the NVIDIA GeForce RTX 5070 Ti with 16GB VRAM, use the --n-gpu-layers 12 flag to offload some layers to the CPU, enable --flash-attn for faster attention computations, and set --tensor-parallelism 1 to utilize the full GPU memory efficiently. This configuration ensures that the model runs smoothly within the 16GB VRAM limit.
Troubleshooting
Out of memory error during inference
Reduce the number of GPU layers by increasing the --n-gpu-layers value, e.g., --n-gpu-layers 24.
Slow token generation speed
Ensure that --flash-attn is enabled and that the CUDA path is correctly set in Ollama.
Model fails to load
Verify that the model file is downloaded correctly and that the Ollama runtime is properly installed and configured.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can be used if you need more customization options or specific features not available in Ollama. LM Studio is ideal for a user-friendly interface, llama.cpp offers fine-grained control over quantization and performance settings, and Jan is suitable for distributed training and large-scale deployments. However, Ollama provides a balanced approach with good performance and ease of use for most users on the NVIDIA GeForce RTX 5070 Ti.
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FAQ (20)
What GPU do I need to run Mistral Nemo Base 12B?
To run Mistral Nemo Base 12B, you need a GPU with at least 7.7 GB of VRAM, but 24.5 GB is recommended for better performance, especially with higher quantization levels.
Is Mistral Nemo Base 12B good for coding?
Mistral Nemo Base 12B is a versatile model that can handle coding tasks well, thanks to its large context length of 131,072 tokens and strong language understanding capabilities.
Mistral Nemo Base 12B vs Llama 3.1 8B?
Mistral Nemo Base 12B has more parameters (12B vs 8B) and a longer context length (131,072 vs typically 2,048 tokens), making it more powerful for complex tasks but requiring more VRAM.
Can I run Mistral Nemo Base 12B on a Mac?
Yes, you can run Mistral Nemo Base 12B on a Mac with an NVIDIA GPU and sufficient VRAM. Ensure you have the necessary drivers and CUDA support installed.
How much VRAM does Mistral Nemo Base 12B need?
Mistral Nemo Base 12B requires between 7.7 GB and 24.5 GB of VRAM, depending on the quantization level used. Higher quantization reduces VRAM usage but may affect performance.
Is Mistral Nemo Base 12B censored?
No, Mistral Nemo Base 12B is naturally uncensored, allowing it to generate content without predefined restrictions.
Is Mistral Nemo Base 12B commercial-use allowed?
Yes, Mistral Nemo Base 12B is licensed under Apache 2.0, which allows commercial use as long as you comply with the license terms.
Mistral Nemo Base 12B context length?
Mistral Nemo Base 12B has a context length of 131,072 tokens, making it suitable for handling very long sequences of text.
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