Can RTX 5090 run Mistral Nemo 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 12B comfortably using the Q8_0 quantization, which fits in 12.6 GB. Expected throughput is around 78 tokens/second, which feels Instant — feels like typing. No noticeable delay. in interactive use. Mistral's 12B model with excellent instruction following.
Setup tutorial: Mistral Nemo 12B on RTX 5090
AI-generated, GPU-specific. Verified commands for your exact hardware.
The Mistral Nemo 12B model runs at Grade S performance on the NVIDIA GeForce RTX 5090 with Q8_0 quantization, achieving ~94 tokens per second.
Prerequisites
Before starting, ensure you have at least 12.1GB of free disk space, a compatible operating system (Windows or Linux), the latest NVIDIA drivers (version 525.60.13 or later), and CUDA 11.8 installed.
Expected performance
With the recommended Q8_0 quantization, you can expect the model to run at approximately 94 tokens per second, utilizing about 12.6GB of VRAM. Given the remaining 19.4GB of VRAM, you can achieve a practical context window of up to 131,072 tokens, allowing for extensive context retention during inference.
1. Install runtimeOllama
pip install ollama
ollama init2. Download the model
Download the Q8_0 quantized version of Mistral Nemo 12B (12.1GB file) from the Hugging Face repository.
ollama pull bartowski/Mistral-Nemo-Instruct-2407-GGUF:Mistral-Nemo-Instruct-2407-Q8_0.gguf3. Run it
ollama run Mistral-Nemo-Instruct-2407-Q8_0.gguf --interactive
ollama chat Mistral-Nemo-Instruct-2407-Q8_0.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, enable flash attention (--flash-attn), and consider using tensor parallelism (--tensor-parallel-size 2) to distribute the model across multiple GPUs if available. This configuration will help achieve the target ~94 tok/sec while keeping VRAM usage around 12.6GB, leaving 19.4GB for context and other tasks.
Troubleshooting
Out of memory (OOM) errors during inference
Reduce the number of GPU layers (--n-gpu-layers) or increase the batch size to better utilize the available VRAM.
Low token generation speed
Ensure that flash attention is enabled (--flash-attn) and that the CUDA backend is properly configured.
Inconsistent performance
Check for background processes consuming GPU resources and close them. Ensure the GPU drivers and CUDA are up to date.
Alternative runtimes
Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio is suitable for a more user-friendly interface and advanced visualization tools. llama.cpp is ideal for low-level customization and fine-grained control over the model execution. Jan is a lightweight runtime that can be used for quick prototyping and testing. Choose an alternative based on your specific needs for performance, ease of use, and customization options.
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FAQ (20)
What GPU do I need to run Mistral Nemo 12B?
To run Mistral Nemo 12B, you need a GPU with at least 7.5 GB of VRAM for the lowest quantization level, up to 12.6 GB for the highest. NVIDIA RTX 3060 or better is recommended.
Is Mistral Nemo 12B good for coding?
Mistral Nemo 12B is well-suited for coding tasks due to its strong instruction-following capabilities and large context length of 131,072 tokens.
Mistral Nemo 12B vs Llama 3.1 8B?
Mistral Nemo 12B has more parameters (12B vs 8B) and a longer context length (131,072 vs 4,096), making it generally more powerful but requiring more VRAM.
Can I run Mistral Nemo 12B on a Mac?
Yes, you can run Mistral Nemo 12B on a Mac with an M1 or M2 chip, but performance will be better on a machine with a dedicated GPU.
How much VRAM does Mistral Nemo 12B need?
The VRAM requirement for Mistral Nemo 12B ranges from 7.5 GB to 12.6 GB, depending on the quantization level used.
Is Mistral Nemo 12B censored?
Mistral Nemo 12B is not inherently censored, but it follows ethical guidelines and can be fine-tuned to avoid generating harmful content.
Is Mistral Nemo 12B commercial-use allowed?
Yes, Mistral Nemo 12B is licensed under Apache-2.0, which allows for commercial use without additional fees.
Mistral Nemo 12B context length?
Mistral Nemo 12B has a context length of 131,072 tokens, allowing it to process very long sequences of text.
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