Danube 3 4B is a 4 billion parameter language model developed by H2O.ai, designed for efficient local deployment with a context length of 8192 tokens. This model excels in generating coherent and contextually relevant text, making it suitable for tasks such as content creation, summarization, and conversational agents. Its architecture, named "danube," is optimized for performance, allowing it to handle complex natural language processing tasks with a relatively low VRAM requirement of 2.7–4.4 GB.
In its size class, Danube 3 4B stands out for its efficiency and performance. It punches above its weight by delivering high-quality outputs while maintaining a manageable resource footprint. This makes it an excellent choice for users who need a powerful yet lightweight model that can run on a variety of hardware setups, including laptops and mid-range desktops. Ideal users include developers, content creators, and small businesses looking for a versatile AI tool that doesn’t require high-end GPUs. The availability of quantizations like Q4_K_M and Q8_0 further enhances its efficiency, making it accessible even on systems with limited resources.
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| Q4_K_M | 4.5 | 2.23 GB | 2.73 GB | 3.23 GB | 85% |
| Q8_0 | 8 | 3.922 GB | 4.42 GB | 4.92 GB | 98% |
Context window & KV cache
Adds 0.66 GB to VRAMLong chats and RAG inputs cost real memory. Drag to see how 32K vs 128K context shifts your grade.
Model native max: 8K tokens. KV-cache estimate is approximate (±30 %); real usage depends on attention layout.
How to run Danube 3 4B
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/h2o-danube3-4b-chat-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 Danube 3 4B on actual hardware.
No community runs yet for this model. Be the first to submit your numbers.
Self-host serving plan
Want to host Danube 3 4Bfor many users? Or run it on a card that’s technically too small? Slide the knobs.
VRAM needed
3.7 GB
2.7 GB weights + 0.5 GB KV
Aggregate tok/s
63
across 1 user
Per-user tok/s
63
4 B dense
✅ Fits in 24 GB VRAM with 20.3 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 Danube 3 4B?
Danube 3 4B requires 2.73 GB VRAM minimum with Q4_K_M quantization. For full precision you need 4.42 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 Danube 3 4B?
To run Danube 3 4B, you need a GPU with at least 2.7 GB of VRAM for the lowest quantization level, up to 4.4 GB for higher quantization levels.
Is Danube 3 4B good for coding?
Danube 3 4B is suitable for coding tasks, but its performance may vary depending on the complexity of the code and the context length required.
Danube 3 4B vs Llama 3.1 8B?
Danube 3 4B has 4 billion parameters, while Llama 3.1 8B has 8 billion parameters. Llama 3.1 8B may offer better performance but requires more VRAM and computational resources.
Can I run Danube 3 4B on a Mac?
Yes, you can run Danube 3 4B on a Mac, provided your Mac has a compatible GPU with sufficient VRAM (2.7 GB to 4.4 GB depending on quantization).
How much VRAM does Danube 3 4B need?
Danube 3 4B requires between 2.7 GB and 4.4 GB of VRAM, depending on the quantization level used.
Is Danube 3 4B censored?
Danube 3 4B is not inherently censored, but it adheres to ethical guidelines set by H2O.ai to ensure responsible use.
Is Danube 3 4B commercial-use allowed?
Yes, Danube 3 4B is licensed under Apache-2.0, which allows commercial use as long as you comply with the license terms.
Danube 3 4B context length?
Danube 3 4B has a context length of 8192 tokens, allowing it to handle longer sequences of text.
Does Danube 3 4B support function calling?
Danube 3 4B supports function calling, enabling it to interact with external systems and APIs effectively.
Danube 3 4B quantization options?
Danube 3 4B supports various quantization options, including 4-bit, 8-bit, and full precision, to optimize performance and VRAM usage.
Can Danube 3 4B run on CPU?
While Danube 3 4B can run on a CPU, it will be significantly slower compared to running on a GPU with the same specifications.
Danube 3 4B fine-tuning?
Danube 3 4B can be fine-tuned using frameworks like Hugging Face Transformers, allowing you to adapt it to specific tasks or domains.
Danube 3 4B system requirements?
Danube 3 4B requires a GPU with 2.7 GB to 4.4 GB of VRAM, at least 8 GB of RAM, and a modern CPU. It also needs a compatible operating system and drivers.
Danube 3 4B performance benchmark?
Performance benchmarks for Danube 3 4B show it can process around 100-150 tokens per second on a mid-range GPU, with higher throughput on more powerful hardware.
Danube 3 4B for RAG?
Danube 3 4B can be used for Retrieval-Augmented Generation (RAG) tasks, leveraging its large context length and function calling capabilities to integrate with external data sources.
Danube 3 4B for agents?
Danube 3 4B is well-suited for creating conversational agents due to its ability to handle long contexts and perform complex reasoning tasks.
Danube 3 4B for coding vs general?
Danube 3 4B performs well in both coding and general tasks, though its performance in coding may be slightly less optimized compared to specialized coding models.
Danube 3 4B vs ChatGPT?
Danube 3 4B is smaller (4B parameters) compared to ChatGPT (175B parameters), making it more resource-efficient but potentially less powerful in complex tasks.
Danube 3 4B download size?
The download size for Danube 3 4B varies depending on the quantization level, ranging from approximately 2 GB for 4-bit quantization to 8 GB for full precision.
Best quant for Danube 3 4B?
The best quantization for Danube 3 4B depends on your hardware and use case. 4-bit quantization offers the best balance between performance and VRAM efficiency for most users.