Can RTX 3090 Ti run Dolphin Mistral 24B (Venice Edition)?

Yes — runs locally

~22 tok/sec · Good — slight pause, then text streams smoothly.

Your VRAM

24 GB

Model size

24B

Best quant

Q4_K_M

VRAM needed

14.9 GB

The verdict

The RTX 3090 Ti (24 GB VRAM) handles Dolphin Mistral 24B (Venice Edition) comfortably using the Q4_K_M quantization, which fits in 14.9 GB. Expected throughput is around 22 tokens/second, which feels Good — slight pause, then text streams smoothly. in interactive use. Headline 24B uncensored pick — top community engagement among uncensored models on HF. Steerable assistant on Mistral-Small-24B base. Apache 2.0.

Setup tutorial: Dolphin Mistral 24B (Venice Edition) on RTX 3090 Ti

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

TL;DR

Run Dolphin Mistral 24B (Venice Edition) on an NVIDIA GeForce RTX 3090 Ti with Q4_K_M quantization for Grade S performance at ~50 tok/sec.

Prerequisites

Before starting, ensure you have at least 15GB of free disk space, a 64-bit version of Windows or Linux, NVIDIA driver version 470.82.01 or later, and CUDA 11.4 or later installed.

Expected performance

With the Q4_K_M quantization, you can expect the model to run at approximately 50 tokens per second, using 14.9GB of VRAM. This leaves about 9.1GB of VRAM for context, enabling a practical context window of around 16,384 tokens.

1. Install runtimeOllama

pip install ollama
ollama init

2. Download the model

Download the Q4_K_M quantized model (14.4GB file) from Hugging Face.

ollama pull bartowski/Dolphin-Mistral-24B-Venice-Edition-GGUF:Dolphin-Mistral-24B-Venice-Edition-Q4_K_M.gguf

3. Run it

ollama run Dolphin-Mistral-24B-Venice-Edition-Q4_K_M --n-gpu-layers 24 --flash-attn
ollama chat Dolphin-Mistral-24B-Venice-Edition-Q4_K_M

4. Optimize for RTX 3090 Ti

For optimal performance on the NVIDIA GeForce RTX 3090 Ti with 24GB VRAM, use the --n-gpu-layers 24 flag to utilize all available GPU layers. Enable flash attention (--flash-attn) to speed up inference and reduce memory usage. With 14.9GB VRAM used by the model, you will have approximately 9.1GB of VRAM left for context, allowing for a practical context window of around 16,384 tokens.

Troubleshooting

Out of memory error during inference

Reduce the number of GPU layers: ollama run Dolphin-Mistral-24B-Venice-Edition-Q4_K_M --n-gpu-layers 16 --flash-attn

Slow inference speed

Ensure flash attention is enabled: ollama run Dolphin-Mistral-24B-Venice-Edition-Q4_K_M --n-gpu-layers 24 --flash-attn

Model not found

Verify the model path and re-run the pull command: ollama pull bartowski/Dolphin-Mistral-24B-Venice-Edition-GGUF:Dolphin-Mistral-24B-Venice-Edition-Q4_K_M.gguf

Alternative runtimes

Alternative runtimes like LM Studio, llama.cpp, and Jan can be used for more advanced configurations or different use cases. LM Studio offers a user-friendly interface and is suitable for those who prefer a GUI. llama.cpp provides more fine-grained control over model execution and is ideal for users who need to tweak parameters extensively. Jan is another lightweight runtime that can be useful for deployment scenarios where minimal dependencies are required. For the NVIDIA GeForce RTX 3090 Ti, Ollama is generally recommended due to its ease of use and good performance out-of-the-box.

Full Dolphin Mistral 24B (Venice Edition) details →

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FAQ (20)

What GPU do I need to run Dolphin Mistral 24B (Venice Edition)?

To run Dolphin Mistral 24B (Venice Edition), you need a GPU with at least 14.9 GB of VRAM for the lowest quantization level, up to 48.5 GB for the highest.

Is Dolphin Mistral 24B (Venice Edition) good for coding?

Dolphin Mistral 24B (Venice Edition) is well-suited for coding tasks due to its large context length of 32,768 tokens and strong community engagement, making it a reliable choice for code generation and debugging.

Dolphin Mistral 24B (Venice Edition) vs Llama 3.1 8B?

Dolphin Mistral 24B (Venice Edition) has more parameters (24B vs 8B) and a longer context length (32,768 vs typically shorter for Llama 3.1 8B), making it more powerful but requiring more VRAM and computational resources.

Can I run Dolphin Mistral 24B (Venice Edition) on a Mac?

Yes, you can run Dolphin Mistral 24B (Venice Edition) on a Mac with a compatible GPU that meets the VRAM requirements (14.9 GB to 48.5 GB). Ensure your Mac has the necessary drivers and software installed.

How much VRAM does Dolphin Mistral 24B (Venice Edition) need?

Dolphin Mistral 24B (Venice Edition) requires between 14.9 GB and 48.5 GB of VRAM, depending on the quantization level used.

Is Dolphin Mistral 24B (Venice Edition) censored?

No, Dolphin Mistral 24B (Venice Edition) is an uncensored model, allowing for a wide range of content generation without built-in restrictions.

Is Dolphin Mistral 24B (Venice Edition) commercial-use allowed?

Yes, Dolphin Mistral 24B (Venice Edition) is licensed under Apache 2.0, which allows for commercial use as long as you comply with the terms of the license.

Dolphin Mistral 24B (Venice Edition) context length?

Dolphin Mistral 24B (Venice Edition) has a context length of 32,768 tokens, allowing it to process and generate long sequences of text effectively.

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