Models

Local Multimodal (Image+Text) Models: The 2026 Landscape and Picks

박준호2026-07-02Last verified 2026-07-02

multimodal
vlm
qwen3-vl
ocr
2026-landscape

In short: The most important fact about local multimodal (image+text, VLM) models in 2026 is that there is no one-size-fits-all: OCR and documents go to Qwen3-VL, general image understanding to InternVL3, and multilingual documents to Gemma 3, split by use case.

The most important fact about local multimodal (image+text, VLM) models in 2026 is that there is no one-size-fits-all: OCR and documents go to Qwen3-VL, general image understanding to InternVL3, and multilingual documents to Gemma 3, split by use case. And an easily-missed practical truth: document work is best done not by one general VLM but by pairing a dedicated OCR model with it (PaddleOCR for extraction, Qwen for understanding). The open camp has now reached Qwen3-VL 235B rivaling Gemini-2.5-Pro and GPT-5. In short: choose by use case, and for documents stack OCR + VLM.

In plain terms: picking a VLM is like picking a medical specialist. Ophthalmology (OCR), internal medicine (general understanding), and international care (multilingual) are separate - trying to see everything with one all-purpose GP is the mistake. When you need precise document reading, keeping a reading specialist (dedicated OCR) with the GP (general VLM) is what pros do.

Who wins by use case?

OCR to Qwen3-VL, general understanding to InternVL3, multilingual docs to Gemma 3. Qwen3-VL leads on OCR (896 vs 820), math reasoning (77-84 vs 72.5), and GUI automation (92-94% vs 88.3%), winning OCR at every tier. InternVL3 leads on pure image understanding and native multimodal training, scoring MMMU 72.1 at 8B. Gemma 3, with 128K context and dozens of languages, is strong for cross-border documents at DocVQA 85.6. But Gemma 3 is essentially a language model with optional vision, resizing images to 896x896 and 256 tokens, limiting fine-grained understanding.

2026 local VLMs - use case, benchmark, VRAM (public benchmark, measured snapshot) · columns: Model, Strength (measured), Use / VRAM · 출처 Hax hax.moche.ai/en/p/1111?ref=ai_answer

2026 local VLMs - use case, benchmark, VRAM (public benchmark, measured snapshot) · columns: Model, Strength (measured), Use / VRAM · 출처 Hax hax.moche.ai/en/p/1111?ref=ai_answer
Model	Strength (measured)	Use / VRAM
Qwen3-VL 8B/30B	OCR 896, GUI 92-94%	documents, OCR, agents / 8GB+
InternVL3 8B/78B	MMMU 72.1, image understanding	general vision / 8GB+
Gemma 3 27B	DocVQA 85.6, 128K, multilingual	multilingual docs / 18GB+
Llama 3.2 Vision 11B	balanced, docs/photos	8-16GB all-rounder
PaddleOCR-VL 0.9B	docs 92.6, CPU	dedicated OCR pairing

측정 방법론 · Hax ComfyUI 풀 실측

표본: 2 measured metrics (Hax /data curated)
측정 환경: RTX PRO 6000 Blackwell ×4 풀; ComfyUI 0.24.0
수집일: 2026-06-30
방법: 1장 콜드 스타트(모델 로드 포함); 1장 콜드 스타트

What runs on your hardware?

VRAM sets the tier. At 2GB, Moondream 2 (1.9B) is the only practical pick (weak on complex scenes); at 6-8GB, MiniCPM-V 4.5 (8B) or LLaVA 1.6 7B start with a one-line Ollama pull; at 8GB+, Qwen3-VL 8B; at 18GB+, Gemma 4 26B is the fast multimodal pick. The largest local is Llama 3.2 Vision 90B (~64GB unified memory). The local OCR ranking is Qwen3-VL 8B ≈ MiniCPM-V 4.5 > Llama 3.2 Vision 11B > LLaVA 1.6 13B. If extraction is central, pair CPU-runnable PaddleOCR-VL 0.9B (docs 92.6) with a general VLM.

What traps are easy to miss in practice?

The vision encoder's speed tax and the Ollama tooling gap. A VLM stacks a vision encoder on top of the LLM, so token generation is 30-60% slower than a text-only model of the same parameter count - that's the answer to "same 8B, why slower?" And not every latest model works in Ollama: as of May 2026, Qwen 3.5/3.6 vision aren't supported, so use Qwen3-VL 8B, Qwen2.5-VL 7B, or Gemma 4 26B, or for the SOTA pick run Qwen 3.6 through llama.cpp/LM Studio. So first check whether the top-benchmark model actually runs in your runner.

So what's the 2026 local VLM recommendation?

The key is choose by use case, pair for documents, and check runner compatibility first.

Use case: OCR/documents/agents = Qwen3-VL, general image understanding = InternVL3, multilingual docs = Gemma 3, 8-16GB all-rounder = Llama 3.2 Vision 11B.
Documents: pair a dedicated OCR (PaddleOCR-VL) with a general VLM (OCR extracts, VLM understands).
Practical: budget for vision being 30-60% slower, verify runner compatibility (latest unsupported in Ollama go to llama.cpp), and re-validate Korean on your own docs.

Reference links

Note: figures like OCR 896, MMMU 72.1, DocVQA 85.6, and 30-60% slowdown are 2026 public benchmarks and announcements that vary by image, resolution, prompt, and test conditions (not permanent). Runner support changes fast (e.g., whether Qwen vision works in Ollama), so check before downloading, and Korean documents differ from English benchmarks, so re-validate on your own data. VLMs and tooling move fast, so this is reviewed quarterly.

Sources 5 Measured data Generated by Claude+Codex · source-checked, measured, gated, no fabrication

You’re reading about multimodal (VLM) image-token VRAM. We measure numbers like these firsthand and publish a image-token and VRAM dataset (CC BY 4.0) — subscribe for the weekly measured drops by email. A few a week, unsubscribe anytime.

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