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Models (4)

Larger vision-language Llama variant, competitive with the proprietary multimodal frontier on standard image-understanding benchmarks. Drops in as a vision upgrade where 11B isn't sharp enough. Requires substantial GPU memory in fp16; most teams will run it quantized or on multi-GPU. A natural pairing with retrieval pipelines that fetch image-rich chunks alongside text.

Context: 128K
License: llama-3
VRAM Q4: 54 GB

Llama 3.2 11B Vision

11B

Llama 3's first vision-language model. Image understanding via a separately-trained ViT adapter bolted onto Llama 3 weights. Useful for OCR-adjacent workloads, document understanding, and image captioning at a permissive licence. The 11B size makes it cheap to host. Combined with the 128K text context, it handles long PDF-with-images workflows comfortably on a single 4090.

Context: 128K
License: llama-3
VRAM Q4: 6.6 GB

Yi VL 34B

34B

Vision-language variant of Yi 34B. Image-text reasoning via an MLP adapter on a CLIP encoder. Useful for bilingual EN/中 multimodal workloads where the major Western vision-language models underperform on Chinese text in images.

Context: 4K
License: apache-2-0
VRAM Q4: 20.4 GB

Llama 4 Scout 17B (16E)

17B

Meta's April 2025 mixture-of-experts release. 17B active parameters across 16 experts (109B total). Natively multimodal with an unprecedented 10M-token context window — a leap far beyond Llama 3's 128K. Scout was designed to run on a single GPU at Q4 while beating Llama 3.3 70B on reasoning and multilingual benchmarks. The Llama 4 licence tightened acceptable-use provisions vs Llama 3.

Context: 10.0M
License: llama-4
VRAM Q4: 10.2 GB

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