Can RTX 5060 Ti 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 5060 Ti (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 5060 Ti

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

TL;DR

The Dolphin Mistral 24B (Venice Edition) runs on the NVIDIA GeForce RTX 5060 Ti with a grade B performance, using the Q4_K_M quantization, achieving approximately 33 tokens per second.

Prerequisites

Before starting, ensure you have at least 15GB 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 or later installed.

Expected performance

With the Q4_K_M quantization, you can expect the model to run at approximately 33 tokens per second, consuming around 14.9GB of VRAM. This leaves about 1.1GB of VRAM for context, allowing for a practical context window of several thousand tokens.

1. Install runtimeOllama

pip install ollama
ollama config set runtime cuda

2. Download the model

Download the Q4_K_M quantized version of the model, which is 14.4GB in size.

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
ollama chat Dolphin-Mistral-24B-Venice-Edition-Q4_K_M

4. Optimize for RTX 5060 Ti

For optimal performance on the NVIDIA GeForce RTX 5060 Ti with 16GB VRAM, use the --n-gpu-layers parameter to offload some layers to CPU if needed. Enable flash attention with --flash-attn to reduce memory usage and improve speed. Given the 14.9GB VRAM requirement, you will have about 1.1GB of VRAM left for context, which should support a practical context window of several thousand tokens.

Troubleshooting

Out of memory errors during inference

Reduce the number of GPU layers with --n-gpu-layers or enable flash attention with --flash-attn to optimize memory usage.

Slow inference speed

Ensure that the CUDA runtime is correctly configured with 'ollama config set runtime cuda'. Also, check if the latest NVIDIA drivers are installed.

Model fails to load

Verify the integrity of the downloaded model file and try downloading it again. Ensure that the Ollama runtime is up to date with 'pip install --upgrade ollama'.

Alternative runtimes

Alternative runtimes include LM Studio, llama.cpp, and Jan. LM Studio provides a more user-friendly interface and is suitable for those who prefer a graphical environment. llama.cpp offers more fine-grained control over model execution and is ideal for advanced users. Jan is a lightweight runtime that is easy to set up but may lack some of the advanced features of Ollama. Choose based on your specific needs and comfort level with command-line tools.

Full Dolphin Mistral 24B (Venice Edition) details →

Other models that run great on RTX 5060 Ti

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.

Want personalized recommendations for your exact setup? Detect my hardware →