Can RTX 5060 Ti run Mistral Nemo Base 12B?
Yes — runs locally
~48 tok/sec · Fast — smooth conversation. Responses feel real-time.
The verdict
The RTX 5060 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 5060 Ti
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Mistral Nemo Base 12B on an NVIDIA GeForce RTX 5060 Ti with Q4_K_M quantization for Grade S performance at ~77 tok/sec.
Prerequisites
Before starting, ensure you have at least 10GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 525.60.13 or later) with CUDA 11.8 installed.
Expected performance
With the Q4_K_M quantization, you can expect the model to run at approximately 77 tokens per second, consuming about 7.7GB of VRAM. Given the remaining 8.3GB of VRAM, you should be able to achieve a practical context window of up to 64K tokens, depending on the complexity of the input.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the Q4_K_M quantized version of Mistral Nemo Base 12B (7.2GB file size) 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.gguf --interactive
ollama chat Mistral-Nemo-Base-2407-Q4_K_M.gguf4. Optimize for RTX 5060 Ti
For optimal performance on the NVIDIA GeForce RTX 5060 Ti with 16GB VRAM, use the --n-gpu-layers flag to offload layers to the GPU, enable flash attention with --flash-attn, and consider using tensor parallelism with --tensor-parallel-size 2. This configuration will help maintain the ~77 tok/sec speed while keeping VRAM usage around 7.7GB, leaving 8.3GB for context and other operations.
Troubleshooting
Out of memory error during inference
Reduce the number of GPU layers with --n-gpu-layers or decrease the batch size.
Slow inference speed
Enable flash attention with --flash-attn and ensure your CUDA drivers are up to date.
Model fails to load
Check the integrity of the downloaded model file and try re-downloading it.
Alternative runtimes
For users preferring different runtimes, LM Studio offers a graphical interface and is suitable for those who prefer a GUI. llama.cpp is a lightweight option that supports a wide range of quantizations and is ideal for systems with limited resources. Jan is another runtime that provides flexibility and is optimized for specific hardware configurations. Choose based on your specific needs and preferences.
Other models that run great on RTX 5060 Ti
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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