Can RTX 4080 SUPER run Dolphin Mistral 24B (Venice Edition)?

Yes — runs locally

~0 tok/sec · Cannot run — model too large for this GPU

Your VRAM

16 GB

Model size

24B

Best quant

Q4_K_M

VRAM needed

14.9 GB

The verdict

The RTX 4080 SUPER (16 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 0 tokens/second, which feels Cannot run — model too large for this GPU 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 4080 SUPER

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

TL;DR

The Dolphin Mistral 24B (Venice Edition) runs on an NVIDIA GeForce RTX 4080 SUPER with a grade B performance, using the Q4_K_M quantization, achieving ~33 tok/sec.

Prerequisites

Before starting, ensure you have at least 14.4GB of free disk space, a compatible operating system (Windows or Linux), and the latest NVIDIA drivers (version 525.60.11 or later) with CUDA 11.8 installed.

Expected performance

With the Q4_K_M quantization, you can expect the model to run at approximately 33 tokens per second, using around 14.9GB of VRAM. This leaves about 1.1GB of VRAM for context, allowing for a practical context window of up to 32,768 tokens, depending on the complexity of the input.

1. Install runtimeOllama

pip install ollama
ollama config set runtime cuda

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 --context-length 32768 --n-gpu-layers 32 --flash-attn
ollama chat Dolphin-Mistral-24B-Venice-Edition-Q4_K_M

4. Optimize for RTX 4080 SUPER

For optimal performance on the NVIDIA GeForce RTX 4080 SUPER with 16GB VRAM, use the --n-gpu-layers 32 flag to offload some layers to the CPU, enabling flash attention (--flash-attn) to reduce memory usage and improve speed. This configuration will allow you to achieve the target ~33 tok/sec while keeping VRAM usage within the 16GB limit.

Troubleshooting

Out of memory error during inference

Reduce the number of GPU layers using --n-gpu-layers 24 or lower.

Slow inference speed

Ensure that flash attention is enabled with --flash-attn and that the CUDA runtime is correctly configured.

Model not found

Verify that the model was successfully downloaded and is available in the Ollama model directory.

Alternative runtimes

For users preferring different runtimes, LM Studio offers a user-friendly GUI but may require more system resources. llama.cpp provides a lightweight, highly customizable option, ideal for fine-tuning performance settings. Jan is another lightweight runtime that supports a wide range of models but may lack some of the advanced features of Ollama. Choose based on your specific needs for performance, ease of use, and customization.

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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