Can RTX 4090 run Mistral Nemo Base 12B?

Yes — runs locally

~66 tok/sec · Instant — feels like typing. No noticeable delay.

Your VRAM

24 GB

Model size

12B

Best quant

Q4_K_M

VRAM needed

7.7 GB

The verdict

The RTX 4090 (24 GB VRAM) handles Mistral Nemo Base 12B comfortably using the Q4_K_M quantization, which fits in 7.7 GB. Expected throughput is around 66 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 4090

AI-generated, GPU-specific. Verified commands for your exact hardware.

TL;DR

Run Mistral Nemo Base 12B on an NVIDIA GeForce RTX 4090 with Q4_K_M quantization for Grade S performance at ~116 tok/sec.

Prerequisites

Before starting, ensure you have at least 10GB of free disk space, a 64-bit version of Windows or Linux, NVIDIA driver version 525.60 or later, and CUDA 11.8 or later installed.

Expected performance

With the Q4_K_M quantization, you can expect ~116 tok/sec performance, utilizing 7.7GB of VRAM, leaving 16.3GB of headroom for context. This allows for a practical context window of up to 131072 tokens, leveraging the full 128K context length of the model.

1. Install runtimeOllama

pip install ollama
ollama config set runtime cuda

2. Download the model

Download the Q4_K_M quantized version of Mistral Nemo Base 12B (7.2GB file size).

ollama pull bartowski/Mistral-Nemo-Base-2407-GGUF:Mistral-Nemo-Base-2407-Q4_K_M.gguf

3. Run it

ollama run Mistral-Nemo-Base-2407-Q4_K_M.gguf
ollama chat --model Mistral-Nemo-Base-2407-Q4_K_M.gguf

4. Optimize for RTX 4090

For optimal performance on the NVIDIA GeForce RTX 4090 with 24GB VRAM, use --n-gpu-layers 12 to offload some layers to the CPU, enable flash-attn for faster attention computation, and consider using tensor parallelism with --tensor-parallel-size 2 to further speed up inference without exceeding VRAM limits.

Troubleshooting

Out of memory errors during inference

Reduce the number of GPU layers with --n-gpu-layers 8 or lower, or increase the batch size with --batch-size 16.

Slow inference speeds

Enable flash attention with --flash-attn and use tensor parallelism with --tensor-parallel-size 2.

Model fails to load

Ensure the correct model file is downloaded and the Ollama runtime is configured to use the CUDA backend with 'ollama config set runtime cuda'.

Alternative runtimes

For users preferring a different runtime, LM Studio offers a GUI-based approach suitable for less technical users, while llama.cpp provides more control over quantization and optimization settings, ideal for advanced users. Jan is another lightweight option that may be preferred for systems with limited resources.

Full Mistral Nemo Base 12B details →

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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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