Can RTX 4070 Ti 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 4070 Ti 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 4070 Ti SUPER

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

TL;DR

Run Dolphin Mistral 24B (Venice Edition) on a NVIDIA GeForce RTX 4070 Ti SUPER with Q4_K_M quantization for Grade B performance at ~33 tok/sec.

Prerequisites

Before starting, ensure you have at least 20GB 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 the model to run at approximately 33 tokens per second, using about 14.9GB of VRAM. This leaves around 1.1GB of VRAM for context, enabling a practical context window of up to 10,000 tokens.

1. Install runtimeOllama

pip install ollama
ollama init

2. Download the model

Download the Q4_K_M quantized model, which is a 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 24 --flash-attn --context-length 32768

4. Optimize for RTX 4070 Ti SUPER

For optimal performance on the NVIDIA GeForce RTX 4070 Ti SUPER with 16GB VRAM, set --n-gpu-layers to 24 to utilize most of the GPU memory without exceeding it. Enable --flash-attn for faster inference and better memory efficiency. Given the 14.9GB VRAM usage, you will have approximately 1.1GB of VRAM left for context, allowing for a practical context window of around 10,000 tokens.

Troubleshooting

Out of memory error during inference

Reduce the number of layers offloaded to the GPU using --n-gpu-layers 16 or lower.

Slow inference speed

Ensure that --flash-attn is enabled and that your CUDA drivers are up to date.

Model fails to load

Verify that the model file has been downloaded correctly and that there are no corruption issues. Try re-downloading the model.

Alternative runtimes

For users preferring different runtimes, consider LM Studio for a more user-friendly interface, llama.cpp for low-level control and customization, or Jan for a lightweight, easy-to-use alternative. Each runtime has its strengths, but Ollama provides a good balance of performance and ease of use for this GPU.

Full Dolphin Mistral 24B (Venice Edition) details →

Other models that run great on RTX 4070 Ti SUPER

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 →