Moondream 2 (vision): Hardware & Business Fit
- Vision
A small, openly-licensed vision-language model for on-device image Q&A and captioning. Capability is limited by size; verify real-world footprint for your inputs.
- Parameters
- ~1.8B
- Context
- ~2K tokens
- Deployment
- local
- VRAM @ 4-bit
- ~1.5GB
What Moondream 2 (vision) is good for
- ▸On-device image Q&A
- ▸Captioning
- ▸Lightweight visual checks
Best quantization choices
Approximate memory per quantization (weights + KV cache at modest context). Treat as ±.
| Quant | ~Memory | When to use |
|---|---|---|
| Q4_K_M | ~1.5GB | Best size/quality trade-off — the usual default for local serving. |
| Q8_0 | ~2.5GB | Higher fidelity; ~1.7× the memory of 4-bit. |
| FP16 | ~4GB | Full precision; largest footprint, best quality. |
Run Moondream 2 (vision) locally
Pull and run with Ollama, or grab the weights from Hugging Face.
$ ollama run moondreamvikhyatk/moondream2Compatible hardware
Devices from our catalog graded for Moondream 2 (vision), best fit first.
- NVIDIA B200 (placeholder)NVIDIA · Datacenter GPUs
Fits at FP16 (~4GB) with ~165GB headroom — about 42 concurrent instances.
FP16 · ~4GBRuns well - Supermicro 8x H100 SuperServerSupermicro · AI Servers
Fits at FP16 (~4GB) with ~559.2GB headroom — about 140 concurrent instances.
FP16 · ~4GBRuns well - Dell PowerEdge XE9680Dell · AI Servers
Fits at FP16 (~4GB) with ~559.2GB headroom — about 140 concurrent instances.
FP16 · ~4GBRuns well - AMD Instinct MI300XAMD · Datacenter GPUs
Fits at FP16 (~4GB) with ~165GB headroom — about 42 concurrent instances.
FP16 · ~4GBRuns well - Cloud B200 (Blackwell profile, to verify)Cloud · Cloud GPU Profiles
Fits at FP16 (~4GB) with ~154.4GB headroom — about 39 concurrent instances.
FP16 · ~4GBRuns well - NVIDIA H200 (141GB)NVIDIA · Datacenter GPUs
Fits at FP16 (~4GB) with ~120.1GB headroom — about 31 concurrent instances.
FP16 · ~4GBRuns well - Cloud H200 141GB (profile)Cloud · Cloud GPU Profiles
Fits at FP16 (~4GB) with ~120.1GB headroom — about 31 concurrent instances.
FP16 · ~4GBRuns well - NVIDIA H100 (80GB)NVIDIA · Datacenter GPUs
Fits at FP16 (~4GB) with ~66.4GB headroom — about 17 concurrent instances.
FP16 · ~4GBRuns well - Cloud H100 80GB (profile)Cloud · Cloud GPU Profiles
Fits at FP16 (~4GB) with ~66.4GB headroom — about 17 concurrent instances.
FP16 · ~4GBRuns well - NVIDIA RTX PRO 6000 BlackwellNVIDIA · Professional GPUs
Fits at FP16 (~4GB) with ~80.5GB headroom — about 21 concurrent instances.
FP16 · ~4GBRuns well
Use inside the AI Business OS
Moondream 2 (vision) suits these AI Business OS agent archetypes:
A model is only the engine. Inside the AI Business OS it is wrapped with permissions, tools, connectors, RAG and audit so it can actually do business work safely — see how the AI Business OS works →
Frequently asked questions
What hardware do I need to run Moondream 2 (vision)?+
At 4-bit you need roughly ~1.5GB of usable memory. The minimum self-hostable option in our catalog is the NVIDIA GeForce RTX 3060 12GB. For a comfortable run we recommend the NVIDIA B200 (placeholder).
Which quantization should I use for Moondream 2 (vision)?+
Q4_K_M is the usual default — the best size/quality trade-off. Step up to Q8_0 or FP16 if you have spare memory and want higher fidelity.
Should I run Moondream 2 (vision) locally or in the cloud?+
Local-first is recommended for Moondream 2 (vision). It fits comfortably on hardware you can own, keeping data private and costs predictable.
Related models
Similar picks — family siblings and nearest-size models of the same kind.
- Qwen2-VL 7B (vision)~7BQwen · Vision / Multimodal
- LLaVA 7B (vision)~7BLLaVA · Vision / Multimodal
- LLaVA-Llama3 8B (vision)~8BLLaVA · Vision / Multimodal
- MiniCPM-V 8B (vision)~8BMiniCPM · Vision / Multimodal
- Llama 3.2 Vision 11B~11BLlama · Vision / Multimodal
- LLaVA 13B (vision)~13BLLaVA · Vision / Multimodal
Use Moondream 2 (vision) inside your AI Business OS
BrainOutput helps you run Moondream 2 (vision) as a private business agent — wrapped with the tools, connectors, RAG and guardrails it needs to do real work on hardware you control.
Use this model in your AI Business OS