Can RTX 4090 run Dolphin Mistral 24B (Venice Edition)?

Yes — runs locally

~34 tok/sec · Fast — smooth conversation. Responses feel real-time.

Your VRAM

24 GB

Model size

24B

Best quant

Q4_K_M

VRAM needed

14.9 GB

The verdict

The RTX 4090 (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 34 tokens/second, which feels Fast — smooth conversation. Responses feel real-time. 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 4090

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

TL;DR

Run Dolphin Mistral 24B (Venice Edition) on an NVIDIA GeForce RTX 4090 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, the latest NVIDIA drivers (version 525.60 or later), and CUDA 11.8 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 around 14.9GB of VRAM. This leaves about 9.1GB of VRAM for context, allowing for a practical context window of up to 16,384 tokens, depending on the complexity of the input.

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.gguf --n-gpu-layers 12 --flash-attn --tensor-parallelism 2

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 CPU, enable --flash-attn for faster attention computation, and set --tensor-parallelism 2 to utilize the full GPU capacity. This configuration ensures that the model runs efficiently within the 24GB VRAM limit.

Troubleshooting

Out of memory error during inference

Reduce the number of GPU layers with --n-gpu-layers 8 or decrease the batch size.

Slow inference speed

Ensure that --flash-attn is enabled and try increasing the tensor parallelism with --tensor-parallelism 4.

Model fails to load

Verify that the model file is correctly downloaded and not corrupted. Re-run the download command.

Alternative runtimes

For users preferring different runtimes, consider LM Studio for a more user-friendly interface, llama.cpp for lower-level control, or Jan for advanced customization options. Each runtime has its own strengths, but Ollama provides a balanced approach for ease of use and performance on the NVIDIA GeForce RTX 4090.

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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