llava-onevision-qwen2-0.5b-ov-hf: Self-Hosting & Deployment Guide
llava-onevision-qwen2-0.5b-ov-hf is an open-weight vision-language model that reads images alongside text.
What is llava-onevision-qwen2-0.5b-ov-hf?
llava-onevision-qwen2-0.5b-ov-hf is an open-weight vision-language model that reads images alongside text, published by llava-hf with open weights at llava-hf/llava-onevision-qwen2-0.5b-ov-hf. LLM API does not route this model today, so there is no endpoint or code sample for it here. What this page does give you is the self-hosting picture: published weight sizes, the VRAM each quantisation needs and the commands to serve it yourself. Every figure is read from the public repository and refreshed weekly. At import it had 626,098 downloads on Hugging Face in the previous 30 days, under the apache-2.0 licence.
How to run llava-onevision-qwen2-0.5b-ov-hf yourself
Open weights (apache-2.0), 894M parameters. Below: how much memory the weights need at each quantization, which hardware fits them, and the commands to serve it.
llava-onevision-qwen2-0.5b-ov-hf VRAM requirements
| Precision / quantization | Weights | Figure | Source |
|---|---|---|---|
| BF16 (full precision) | 1.8 GB | Estimate | 894M params × 2 bytes |
File size = actual download size on Hugging Face. Estimate = parameter count × bytes per weight. Weights only — the KV cache for long contexts needs extra memory; the fit tables below add 15% headroom as a rule of thumb.
GPU requirements: deploy llava-onevision-qwen2-0.5b-ov-hf on your hardware
| Hardware | BF16 (full precision) |
|---|---|
| NVIDIA H200141 GB | 1× |
| NVIDIA H10080 GB | 1× |
| NVIDIA A100 80GB80 GB | 1× |
| NVIDIA L40S48 GB | 1× |
GPUs needed per variant.
| Hardware | BF16 (full precision) |
|---|---|
| AWS p5.48xlarge8× H100 · 640 GB | Fits |
| AWS p4de.24xlarge8× A100 80GB · 640 GB | Fits |
| AWS g6e.12xlarge4× L40S · 192 GB | Fits |
| Google Cloud a3-highgpu-8g8× H100 · 640 GB | Fits |
| Azure ND H100 v58× H100 · 640 GB | Fits |
Whole-instance GPU memory; public instance specs.
Serve it yourself
pip install -U vllm
vllm serve llava-hf/llava-onevision-qwen2-0.5b-ov-hfpip install 'sglang[all]'
python -m sglang.launch_server --model-path llava-hf/llava-onevision-qwen2-0.5b-ov-hfCommands use the official repositories above; vLLM and SGLang expose an OpenAI-compatible endpoint.
Not on LLM.API yet
We do not route this model through our API at the moment. Browse the models you can call today — most workloads have a close match already live.
Not available on LLM API yet
We do not route this model through our API at the moment, so there is no endpoint or code snippet for it yet. Browse the models you can call today — most workloads have a close match already live.
Browse available modelsWhen to Use — When NOT to Use
Use it if...
- You want to run llava-onevision-qwen2-0.5b-ov-hf on your own hardware and keep every prompt inside your network
- The weights are published openly, so you can pin one checkpoint and keep it reproducible
- You want to fine-tune or quantise the model rather than accept a hosted configuration
- You process documents, screenshots or charts where the image must stay in-house
Avoid if...
- LLM API does not route this model today, so there is no endpoint here to call
- You have no GPU capacity: the VRAM figures below are the floor, not a suggestion
- You want zero operations — serving, scaling and upgrades are yours to run
- You need audio or video input; this checkpoint documents text and images only
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