The Danube 3 500M is a lightweight language model developed by H2O.ai, designed for efficient local deployment with a modest 0.5 billion parameters. This model excels in generating coherent and contextually relevant text, making it suitable for tasks such as content creation, chatbot responses, and summarization. With a context length of 8192 tokens, it can handle longer inputs and outputs, which is particularly useful for generating detailed articles or maintaining context in extended conversations. The Apache 2.0 license ensures that it is freely available for both personal and commercial use, adding to its appeal.
In its size class, the Danube 3 500M punches well above its weight. Despite its relatively small parameter count, it delivers impressive performance, often matching or exceeding the capabilities of larger models when it comes to efficiency and speed. The available quantizations, including Q4_K_M and Q8_0, further enhance its efficiency, allowing it to run smoothly on hardware with limited resources. Users can expect it to operate effectively on systems with as little as 0.8 to 1.0 GB of VRAM, making it an excellent choice for those with mid-range or older hardware. This model is ideal for developers, content creators, and businesses looking for a balance between performance and resource consumption, ensuring that high-quality text generation is accessible even on less powerful devices.
| Quantization | Bits | File Size | VRAM Needed | RAM Needed | Quality |
|---|---|---|---|---|---|
| Q4_K_M | 4.5 | 0.296 GB | 0.8 GB | 1.3 GB | 85% |
| Q8_0 | 8 | 0.509 GB | 1.01 GB | 1.51 GB | 98% |
Context window & KV cache
Adds 0.13 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 500M
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
h2oai/h2o-danube3-500m-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 500M 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 500Mfor many users? Or run it on a card that’s technically too small? Slide the knobs.
VRAM needed
1.5 GB
0.8 GB weights + 0.2 GB KV
Aggregate tok/s
500
across 1 user
Per-user tok/s
500
0.5 B dense
✅ Fits in 24 GB VRAM with 22.5 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 500M?
Danube 3 500M requires 0.8 GB VRAM minimum with Q4_K_M quantization. For full precision you need 1.01 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 500M?
Danube 3 500M requires a GPU with at least 0.8 GB to 1.0 GB of VRAM, depending on the quantization level.
Is Danube 3 500M good for coding?
Danube 3 500M is suitable for basic coding tasks due to its small size and efficiency, but it may not handle complex code generation as well as larger models.
Danube 3 500M vs Llama 3.1 8B?
Danube 3 500M is significantly smaller (0.5B parameters) and more resource-efficient compared to Llama 3.1 8B (8B parameters), making it ideal for devices with limited resources.
Can I run Danube 3 500M on a Mac?
Yes, Danube 3 500M can run on a Mac, provided your system meets the minimum VRAM requirements of 0.8 GB to 1.0 GB.
How much VRAM does Danube 3 500M need?
Danube 3 500M requires between 0.8 GB and 1.0 GB of VRAM, depending on the quantization level used.
Is Danube 3 500M censored?
Danube 3 500M is not inherently censored, but its responses are guided by the training data and any post-processing filters you apply.
Is Danube 3 500M commercial-use allowed?
Yes, Danube 3 500M is licensed under Apache-2.0, which allows for both commercial and non-commercial use.
Danube 3 500M context length?
Danube 3 500M supports a context length of up to 8192 tokens, allowing for longer conversations and more detailed inputs.
Does Danube 3 500M support function calling?
Danube 3 500M does not natively support function calling, but you can implement custom logic to handle function calls in your application.
Danube 3 500M quantization options?
Danube 3 500M supports various quantization levels, including 4-bit and 8-bit, to reduce memory usage and improve performance.
Can Danube 3 500M run on CPU?
Yes, Danube 3 500M can run on a CPU, although performance will be slower compared to running on a GPU.
Danube 3 500M fine-tuning?
Danube 3 500M can be fine-tuned using standard fine-tuning techniques, but the process may require more computational resources due to its smaller size.
Danube 3 500M system requirements?
To run Danube 3 500M, you need a system with at least 0.8 GB to 1.0 GB of VRAM, 4 GB of RAM, and a modern CPU or GPU.
Danube 3 500M performance benchmark?
Performance benchmarks for Danube 3 500M show it can process around 100-150 tokens per second on a mid-range GPU, depending on the quantization level.
Danube 3 500M for RAG?
Danube 3 500M can be used for Retrieval-Augmented Generation (RAG) tasks, but its smaller size may limit its effectiveness compared to larger models.
Danube 3 500M for agents?
Danube 3 500M is suitable for creating lightweight conversational agents, especially in resource-constrained environments.
Danube 3 500M for coding vs general?
Danube 3 500M is versatile and can handle both coding and general tasks, but its performance may vary depending on the complexity of the task.
Danube 3 500M vs ChatGPT?
Danube 3 500M is much smaller (0.5B parameters) and more resource-efficient compared to ChatGPT, which has billions of parameters and higher resource requirements.
Danube 3 500M download size?
The download size for Danube 3 500M is approximately 200 MB, depending on the quantization level.
Best quant for Danube 3 500M?
The best quantization level for Danube 3 500M depends on your specific needs, but 4-bit quantization offers a good balance between performance and memory usage.