Dolphin Mistral 24B (Venice Edition) is a powerful language model developed by Cognitive Computations, designed for advanced text generation tasks. With 24 billion parameters, this model excels in generating coherent and contextually rich text across a wide range of applications, including creative writing, content generation, and conversational AI. The model's impressive context length of 32,768 tokens allows it to maintain and build upon long-term context, making it particularly suitable for tasks that require understanding and generating lengthy and complex narratives. The Apache-2.0 license ensures that it is freely available for both commercial and non-commercial use, which is a significant advantage for developers and organizations looking to deploy it without licensing concerns.
In its size class, Dolphin Mistral 24B holds its own, offering a balance between performance and efficiency. While it is not the most lightweight model, it demonstrates strong capabilities in generating high-quality text, often outperforming smaller models in terms of coherence and context retention. The model is available in several quantization formats, including BF16, Q4_K_M, and Q8_0, which can significantly reduce the VRAM requirements, making it more accessible for a variety of hardware setups. Users with GPUs ranging from 14.9 GB to 48.5 GB of VRAM can realistically run this model, making it a versatile choice for both high-end workstations and more modest setups. Ideal users include researchers, developers, and businesses that need a robust language model for generating detailed and contextually accurate text, but who may not have access to the most powerful hardware.
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| BF16 | 16 | 48 GB | 48.5 GB | 49 GB | 100% |
| Q4_K_M | 4.5 | 14.4 GB | 14.9 GB | 15.4 GB | 85% |
| Q8_0 | 8 | 25.44 GB | 25.94 GB | 26.44 GB | 98% |
Context window & KV cache
Adds 1.50 GB to VRAMLong chats and RAG inputs cost real memory. Drag to see how 32K vs 128K context shifts your grade.
Model native max: 32K tokens. KV-cache estimate is approximate (±30 %); real usage depends on attention layout.
How to run Dolphin Mistral 24B (Venice Edition)
Pick a runtime — copy & paste. Commands are pre-filled with this model’s repo.
GUI. Browse → download → chat. MLX on Apple Silicon.
LM Studio home →- 1
Open LM Studio
Go to the 🔍 Search tab.
- 2
Search for
bartowski/Dolphin-Mistral-24B-Venice-Edition-GGUF - 3
Download
Pick the Q4_K_M quant — best balance of size vs. quality.
- 4
Chat
Hit ▶ Load Model and start chatting. Toggle 'Local Server' to expose an OpenAI-compatible API on :1234.
Community benchmarks
Real tokens/sec reports from people running Dolphin Mistral 24B (Venice Edition) on actual hardware.
No community runs yet for this model. Be the first to submit your numbers.
Self-host serving plan
Want to host Dolphin Mistral 24B (Venice Edition)for many users? Or run it on a card that’s technically too small? Slide the knobs.
VRAM needed
16.6 GB
14.9 GB weights + 1.2 GB KV
Aggregate tok/s
10
across 1 user
Per-user tok/s
10
24 B dense
✅ Fits in 24 GB VRAM with 7.4 GB headroom. Pure-GPU inference — full speed.
Throughput is a sub-linear estimate: doubling users adds ~70 % of single-user TPS until ~8, then plateaus on memory bandwidth. MoE models scale concurrency much better because each user activates a different subset of experts.
See It In Action
Real model outputs generated via RunThisModel.com — watch responses stream in real time.
Outputs generated by real AI models via RunThisModel.com. Generation speed shown is from cloud inference. Local speeds vary by hardware — check your device.
how much VRAM do I need to run Dolphin Mistral 24B (Venice Edition)?
Dolphin Mistral 24B (Venice Edition) requires 14.9 GB VRAM minimum with BF16 quantization. For full precision you need 48.5 GB.
which quant should I pick?
Q4_K_M is the best quality/VRAM balance — ~92% of FP16 quality at ~25% the footprint. Q8_0 is near-lossless if you have the headroom.
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.
Does Dolphin Mistral 24B (Venice Edition) support function calling?
Yes, Dolphin Mistral 24B (Venice Edition) supports function calling, enabling it to interact with external systems and APIs for enhanced functionality.
Dolphin Mistral 24B (Venice Edition) quantization options?
Dolphin Mistral 24B (Venice Edition) offers multiple quantization options, including 4-bit, 8-bit, and 16-bit, to balance model size and performance based on your hardware capabilities.
Can Dolphin Mistral 24B (Venice Edition) run on CPU?
While Dolphin Mistral 24B (Venice Edition) can technically run on a CPU, it is highly recommended to use a GPU due to the large number of parameters and high computational demands.
Dolphin Mistral 24B (Venice Edition) fine-tuning?
Dolphin Mistral 24B (Venice Edition) can be fine-tuned for specific tasks or domains using a suitable dataset and training framework, allowing you to tailor its performance to your needs.
Dolphin Mistral 24B (Venice Edition) system requirements?
To run Dolphin Mistral 24B (Venice Edition), you need a GPU with 14.9 GB to 48.5 GB of VRAM, a powerful CPU, at least 64 GB of RAM, and a fast SSD for storage.
Dolphin Mistral 24B (Venice Edition) performance benchmark?
Performance benchmarks for Dolphin Mistral 24B (Venice Edition) vary, but it generally processes around 10-20 tokens per second on a high-end GPU, with lower quantization levels providing better speed.
Dolphin Mistral 24B (Venice Edition) for RAG?
Dolphin Mistral 24B (Venice Edition) is well-suited for Retrieval-Augmented Generation (RAG) tasks due to its large context length and ability to handle complex queries and document retrieval.
Dolphin Mistral 24B (Venice Edition) for agents?
Dolphin Mistral 24B (Venice Edition) can be used to create sophisticated conversational agents and chatbots, leveraging its uncensored nature and extensive context length for natural and engaging interactions.
Dolphin Mistral 24B (Venice Edition) for coding vs general?
Dolphin Mistral 24B (Venice Edition) excels in both coding and general tasks, but its large context length and strong community engagement make it particularly effective for coding, while its versatility supports a wide range of general applications.
Dolphin Mistral 24B (Venice Edition) vs ChatGPT?
Dolphin Mistral 24B (Venice Edition) has a larger context length (32,768 tokens) and is uncensored, offering more flexibility and depth in content generation compared to ChatGPT, which may have stricter content policies and a shorter context length.
Dolphin Mistral 24B (Venice Edition) download size?
The download size of Dolphin Mistral 24B (Venice Edition) varies depending on the quantization level, ranging from approximately 12 GB for 4-bit quantization to 48 GB for 16-bit quantization.
Best quant for Dolphin Mistral 24B (Venice Edition)?
The best quantization level for Dolphin Mistral 24B (Venice Edition) depends on your hardware. For most users, 8-bit quantization provides a good balance between performance and resource usage, while 4-bit is optimal for systems with limited VRAM.