LLaVA-OneVision 7B
By LMMs-Lab · Singapore
Updated 2026-07-13
Overview
An Apache-licensed 7B vision-language model from LMMs-Lab, combining SigLIP SO400M with Qwen2-7B. Handles single images, multi-image inputs, and video at over 170k monthly downloads.
When to pick this model
- Self-hosted VLM apps needing a permissive license
- Multi-image reasoning and short video understanding
- Fine-tuning base for domain-specific vision tasks
- Cost-sensitive image captioning and VQA pipelines
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 5 GB |
| Q5_K_M | 6 GB |
| Q8_0 | 9 GB |
| FP16 (no quantization) | 16 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, LLaVA-OneVision 7B fits an 8 GB consumer card at Q4_K_M (5 GB). Stepping up to Q8_0 nearly doubles the footprint to 9 GB, and unquantized FP16 weights take 16 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, LLaVA-OneVision 7B needs roughly 10 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 10 tokens/sec on entry-level GPUs, on the order of 30 tokens/sec on a mid-range card, and up to 80 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches LLaVA-OneVision 7B to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for LLaVA-OneVision 7B |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Q5_K_M (6 GB used) |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Q8_0 (9 GB used) |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | FP16 (16 GB used) |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | FP16 (16 GB used) |
| 32 GB | RTX 5090 | FP16 (16 GB used) |
Which GPU should you buy to run LLaVA-OneVision 7B?
To run LLaVA-OneVision 7B locally at Q4, you need ~5 GB of VRAM. The best value for this is a RTX 5060 (8 GB VRAM).
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Strengths
- Fully Apache 2.0 with no commercial gotchas
- Genuine multi-image and video support
- Mature ecosystem with strong community traction
- Solid Qwen2-7B language backbone
Limitations
- No official Ollama packaging
- English-first; weaker on non-English vision QA
- Outpaced by Qwen3-VL on most 2025 benchmarks
Typical workloads
In our catalog grid, LLaVA-OneVision 7B is filed under Mature VLM, Multi-image, Video — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: vision-language work — screenshots, charts, scanned documents.
The 32k-token context window covers long chats and mid-sized documents, though very large retrieval workloads will need chunking. The Apache 2.0 license is permissive, so shipping it inside a commercial product raises no special legal questions.
Architecture & training
Architecture: VLM 7B · SO400M + Qwen2-7B · image/multi-image/video
Training: LMMs-Lab (Singapore).
A dependable, truly open VLM for self-hosters who value Apache licensing over the latest leaderboard score.
Quick start
# HuggingFace : lmms-lab/llava-onevision-qwen2-7b-ovOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does LLaVA-OneVision 7B need?
At the recommended Q4_K_M quantization, LLaVA-OneVision 7B needs about 5 GB of VRAM. Q8_0 takes 9 GB, and unquantized FP16 weights take 16 GB.
Can LLaVA-OneVision 7B run without a GPU?
Yes — with roughly 10 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does LLaVA-OneVision 7B support?
LLaVA-OneVision 7B supports a 32k-token context window (32,768 tokens).
Can I use LLaVA-OneVision 7B commercially?
Yes. LLaVA-OneVision 7B is released under Apache 2.0, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is LLaVA-OneVision 7B on consumer hardware?
Our compatibility engine estimates on the order of 30 tokens/sec on a mid-range GPU and up to 80 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of LLaVA-OneVision 7B should I download first?
Start with Q4_K_M (5 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It already fits an 8 GB card at Q5_K_M.