Can RTX 5090 run Mistral Nemo Base 12B?
Yes — runs locally
~78 tok/sec · Instant — feels like typing. No noticeable delay.
The verdict
The RTX 5090 (32 GB VRAM) handles Mistral Nemo Base 12B comfortably using the Q4_K_M quantization, which fits in 7.7 GB. Expected throughput is around 78 tokens/second, which feels Instant — feels like typing. No noticeable delay. 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 5090
AI-generated, GPU-specific. Verified commands for your exact hardware.
The Mistral Nemo Base 12B runs at Grade S on the NVIDIA GeForce RTX 5090 with Q4_K_M quantization, achieving ~154 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 525.85.12 or later) with CUDA 11.8 installed.
Expected performance
With the Q4_K_M quantization, you can expect the model to run at approximately 154 tokens per second, using around 7.7GB of VRAM. This leaves you with about 24.3GB of VRAM headroom, allowing for a practical context window of up to 131072 tokens, which is the maximum supported by the model.
1. Install runtimeOllama
pip install ollama
ollama config set device cuda2. Download the model
Download the Q4_K_M quantized model (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.gguf --interactive
ollama chat Mistral-Nemo-Base-2407-Q4_K_M.gguf4. Optimize for RTX 5090
For optimal performance on the NVIDIA GeForce RTX 5090 with 32GB VRAM, use the --n-gpu-layers parameter to offload layers to the GPU. Set --n-gpu-layers to 32 to utilize the GPU effectively. Enable flash attention with --flash-attn to speed up inference. Given the 32GB VRAM, you can also experiment with tensor parallelism using --tensor-parallel-size 2 to further enhance performance.
Troubleshooting
Out of memory errors during inference
Reduce the --n-gpu-layers value to 24 or lower to free up more VRAM.
Slow inference speed
Ensure that flash attention is enabled with --flash-attn and try increasing the --tensor-parallel-size to 2.
Model fails to load
Verify that the model file has been downloaded correctly and that the Ollama runtime is properly installed and configured with CUDA support.
Alternative runtimes
Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio offers a user-friendly interface and is suitable for those who prefer a graphical environment. llama.cpp provides more fine-grained control over model parameters and is ideal for advanced users. Jan is a lightweight runtime that is easy to set up but may not offer the same level of performance tuning as Ollama. For the NVIDIA GeForce RTX 5090, Ollama is recommended due to its ease of use and strong performance optimizations.
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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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