Can RTX 5080 run Mistral Nemo Base 12B?
Yes — runs locally
~48 tok/sec · Fast — smooth conversation. Responses feel real-time.
The verdict
The RTX 5080 (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 5080
AI-generated, GPU-specific. Verified commands for your exact hardware.
Run Mistral Nemo Base 12B on an NVIDIA GeForce RTX 5080 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 specified configuration, you can expect the model to run at approximately 77 tokens per second, using about 7.7GB of VRAM. The remaining 8.3GB of VRAM can be used for a practical context window of up to 131072 tokens, allowing for long and detailed conversations.
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) from Hugging Face.
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 --n-gpu-layers 32 --flash-attn --tensor-parallelism 2
ollama chat Mistral-Nemo-Base-2407-Q4_K_M4. Optimize for RTX 5080
For optimal performance on the NVIDIA GeForce RTX 5080 with 16GB VRAM, set --n-gpu-layers to 32 to utilize the full GPU memory efficiently. Enable --flash-attn for faster attention computation and set --tensor-parallelism to 2 to distribute the workload across the GPU cores. This configuration will help achieve the target ~77 tok/sec while keeping VRAM usage around 7.7GB, leaving 8.3GB for context.
Troubleshooting
Out of memory error during inference
Reduce --n-gpu-layers to 24 or lower and decrease --tensor-parallelism to 1.
Slow token generation speed
Ensure that --flash-attn is enabled and check your CUDA installation for any issues.
Model fails to load
Verify the integrity of the downloaded model file and try re-downloading it.
Alternative runtimes
Alternative runtimes like LM Studio, llama.cpp, and Jan can also be used. LM Studio is suitable for users who prefer a graphical interface, llama.cpp offers more fine-grained control over optimizations, and Jan is ideal for those who need a lightweight solution. However, Ollama provides a balanced approach with ease of use and good performance, making it the recommended choice for this GPU.
Other models that run great on RTX 5080
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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