Can RTX 3080 Ti run Dolphin Mistral 24B (Venice Edition)?

Yes — runs locally

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

Your VRAM

12 GB

Model size

24B

Best quant

Q4_K_M

VRAM needed

14.9 GB

The verdict

The RTX 3080 Ti (12 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 3080 Ti

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

TL;DR

The Dolphin Mistral 24B (Venice Edition) runs on an NVIDIA GeForce RTX 3080 Ti with a Grade C performance, using the Q4_K_M quantization, achieving ~25 tok/sec.

Prerequisites

Before starting, ensure you have at least 14.4GB of free disk space, a 64-bit version of Windows or Linux, the latest NVIDIA drivers (version 525.60.11 or later), and CUDA 11.7 or later installed.

Expected performance

With the Q4_K_M quantization, you can expect the model to run at approximately 25 tokens per second, consuming around 14.9GB of VRAM. Given the 12GB VRAM limit, the model will utilize the remaining 11.1GB of VRAM for context, allowing for a practical context window of about 8,500 tokens.

1. Install runtimeOllama

pip install ollama
ollama config set cuda_path /usr/local/cuda

2. Download the model

Download the Q4_K_M quantized 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 --model Dolphin-Mistral-24B-Venice-Edition-Q4_K_M.gguf --n-gpu-layers 12 --flash-attn
ollama chat --model Dolphin-Mistral-24B-Venice-Edition-Q4_K_M.gguf

4. Optimize for RTX 3080 Ti

For optimal performance on the NVIDIA GeForce RTX 3080 Ti with 12GB VRAM, use --n-gpu-layers 12 to offload layers to the GPU, enable --flash-attn for efficient attention computation, and consider using tensor parallelism if you have multiple GPUs. This configuration will help maximize the token generation speed while staying within the 12GB VRAM limit.

Troubleshooting

Out of memory error during inference

Reduce the number of GPU layers using --n-gpu-layers 8 or lower, or decrease the context length using --context-length 16384.

Slow token generation speed

Ensure that flash attention is enabled with --flash-attn and that the CUDA path is correctly set in Ollama.

Model fails to load

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

Alternative runtimes

For users preferring different runtimes, LM Studio offers a GUI-based approach suitable for those who prefer a visual interface. llama.cpp is a lightweight C++ implementation that can be compiled and run directly on the GPU, ideal for low-resource environments. Jan is another runtime that supports advanced features like multi-GPU support, which might be useful if you plan to scale up in the future. However, Ollama provides a balanced approach with good performance and ease of use for the NVIDIA GeForce RTX 3080 Ti.

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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