Molmo 72B

Overview

Allen AI's flagship Apache 2.0 VLM built on Qwen2-72B, ranked #2 in human evaluation behind only GPT-4o for visual understanding.

When to pick this model

• On-prem replacement for GPT-4o vision in regulated environments
• High-stakes visual analysis where quality dominates cost
• Research benchmarks demanding open weights at frontier quality
• Document and diagram understanding at scale
• Multi-GPU deployments already provisioned for 70B-class models

VRAM requirements by quantization

Quantization	VRAM required
Q4_K_M (recommended)	42 GB
Q5_K_M	50 GB
Q8_0	78 GB
FP16 (no quantization)	144 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.

Published benchmark scores

Benchmark	Score
MMMU	72.2

Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.

Strengths

• Top-tier vision quality among open-weight VLMs
• Apache 2.0 license with PixMo open training data
• Strong on complex visual reasoning and dense scenes
• Human evaluation second only to GPT-4o

Limitations

• ~42 GB VRAM at Q4 typically requires 2-3 GPUs
• 4096-token context constrains long multimodal sessions
• No official GGUF release complicates llama.cpp use

Architecture & training

Architecture: Dense · 72B vision · based on Qwen2 72B + OpenAI CLIP encoder

Training: AllenAI PixMo dataset, maximal 72B version.

Quick start

ollama run molmo:72b

Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.