Vision-language models are the 2026 workhorse of document AI, multimodal agents and visual search. The Blackwell RTX 5060 Ti 16GB runs Llama 3.2 Vision 11B at 72 tokens per second, Qwen 2.5-VL 7B at 90 t/s, and LLaVA-1.6 comfortably – all with native FP8 for the language tower. This post covers which multimodal LLMs fit in 16 GB, their vision encoder latencies, and the OCR-plus-reasoning workloads they handle well on a Gigagpu UK GPU.
Contents
- Supported models
- VRAM and context
- Vision encoder latency
- End-to-end throughput
- OCR and reasoning workloads
- Deployment
Supported models
All four of the models below fit in 16 GB VRAM with sensible quantisation of the language tower. Vision encoders stay in FP16 because they’re small enough that quantisation saves little and costs accuracy.
| Model | Params | Vision encoder | Max image | Languages |
|---|---|---|---|---|
| Llama 3.2 Vision 11B | 11B | ViT-H/14 (400M) | 1120×1120 | EN + limited |
| Qwen 2.5-VL 7B | 7B | Dynamic-res ViT | up to 4K | 29 incl. CJK |
| LLaVA-1.6 7B | 7B | CLIP-L/14 (300M) | 672×672 | EN primary |
| InternVL 2.5 8B | 8B | InternViT-300M | Dynamic tile | Multi |
VRAM and context
| Model | Precision | Weights | KV + vision | Total |
|---|---|---|---|---|
| Llama 3.2 Vision 11B | FP8 LLM + FP16 ViT | 11.9 GB | 2.2 GB | 14.1 GB |
| Qwen 2.5-VL 7B | FP8 LLM + FP16 ViT | 7.8 GB | 2.6 GB | 10.4 GB |
| LLaVA-1.6 7B | FP8 LLM + FP16 ViT | 7.6 GB | 1.9 GB | 9.5 GB |
| InternVL 2.5 8B | AWQ LLM + FP16 ViT | 5.4 GB | 2.8 GB | 8.2 GB |
Vision encoder latency
Image preprocessing and encoding dominate TTFT for multimodal models. Numbers below are single-image, no batching, measured on the 5060 Ti 16GB.
| Model | Input | Patch tokens | Encode time | Prefill (LLM) |
|---|---|---|---|---|
| Llama 3.2 Vision 11B | 1120×1120 | 6,404 | 180 ms | 420 ms |
| Qwen 2.5-VL 7B | 1280×720 | 1,280-2,560 | 95 ms | 180 ms |
| Qwen 2.5-VL 7B | 3840×2160 (doc) | ~8,000 | 310 ms | 540 ms |
| LLaVA-1.6 7B | 672×672 | 576 | 48 ms | 92 ms |
| InternVL 2.5 8B | dynamic 6 tiles | 1,792 | 110 ms | 210 ms |
End-to-end throughput
For a typical multimodal turn – one image plus a short text prompt, 150-token answer:
| Model | Single t/s | TTFT | Wall clock (150 out) |
|---|---|---|---|
| Llama 3.2 Vision 11B FP8 | 72 | 600 ms | 2.7 s |
| Qwen 2.5-VL 7B FP8 | 90 | 275 ms | 1.9 s |
| LLaVA-1.6 7B FP8 | 115 | 140 ms | 1.4 s |
| InternVL 2.5 8B AWQ | 88 | 320 ms | 2.0 s |
OCR and reasoning workloads
Qwen 2.5-VL 7B is the standout for document AI: dynamic resolution means it can ingest an A4 PDF page at 300 dpi and pull structured tables out. Pairing it with PaddleOCR for pre-segmentation gives roughly 15 pages/minute of layout-aware extraction per card.
- Invoice extraction – Qwen 2.5-VL, ~3.8 s/page including reasoning over line items.
- Screenshot QA – LLaVA-1.6, 1.4 s/turn for “what’s wrong with this UI” style prompts.
- Chart understanding – Llama 3.2 Vision 11B excels at plots and diagrams.
- Multilingual OCR – Qwen 2.5-VL covers Chinese, Japanese, Korean, Arabic without extra models.
- UI automation – Qwen 2.5-VL’s grounding tokens return pixel-coordinate bounding boxes.
Deployment
# Qwen 2.5-VL 7B with vLLM
docker run -d --gpus all -p 8000:8000 vllm/vllm-openai:v0.6.3 \
--model Qwen/Qwen2.5-VL-7B-Instruct \
--quantization fp8 \
--max-model-len 32768 \
--limit-mm-per-prompt image=4 \
--gpu-memory-utilization 0.85Deploy multimodal LLMs on a single Blackwell GPU
Qwen 2.5-VL, Llama 3.2 Vision, LLaVA – all in 16 GB. UK dedicated hosting.
Order the RTX 5060 Ti 16GBSee also: Qwen VL benchmark, Llama 3.2 Vision benchmark, PaddleOCR benchmark, vLLM setup, FP8 Llama deployment.
