Llama 3.2 Vision 11B on a Dedicated GPU

Llama 3.2 Vision 11B is an instruction-tuned vision-language model that accepts images plus text and produces text. On our dedicated GPU hosting it sits in a sweet spot: good enough for document Q&A, captioning, and visual reasoning, small enough to fit a single mid-tier card.

Contents

• VRAM
• GPU options
• Deployment
• Use cases

VRAM

Vision models have a second memory consumer: image features during inference. Budget 1-2 GB extra beyond the weight VRAM for image processing.

GPU Options

• RTX 4060 Ti 16GB: FP8 fits with room for small batches
• RTX 3090 24GB: FP16 comfortable
• RTX 5090 32GB: FP16 with high concurrency

Deployment

vLLM supports Llama 3.2 Vision via the OpenAI-compatible multimodal API:

python -m vllm.entrypoints.openai.api_server \
  --model meta-llama/Llama-3.2-11B-Vision-Instruct \
  --max-model-len 8192 \
  --max-num-seqs 8 \
  --limit-mm-per-prompt 'image=1'

Client usage:

response = client.chat.completions.create(
  model="llama3-vision",
  messages=[{
    "role": "user",
    "content": [
      {"type": "text", "text": "What is in this image?"},
      {"type": "image_url", "image_url": {"url": "data:image/png;base64,..."}}
    ]
  }]
)

Use Cases

Llama 3.2 Vision 11B performs well on:

• Document Q&A on scanned pages
• Image captioning
• Visual reasoning about charts, diagrams, screenshots
• UI automation support (screenshot-to-action)

It is weaker at fine-grained OCR and detailed counting. For OCR-heavy workloads pair with PaddleOCR as a preprocessor.

For larger VLM options see Pixtral 12B, Qwen VL 2, and Molmo 7B.