Molmo 7B-D
By Allen AI · United States
Updated 2026-07-13
Overview
Allen AI's Apache-licensed VLM built on Qwen2-7B and CLIP, scoring between GPT-4V and GPT-4o on benchmarks with unique pointing and grounding capabilities.
When to pick this model
- UI automation needing pixel-accurate pointing
- Visual grounding research with permissive licensing
- Image annotation pipelines requiring open data provenance
- Robotics and accessibility tools that need spatial references
- Replacing GPT-4V in workflows that demand on-prem deployment
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 5 GB |
| Q5_K_M | 6 GB |
| Q8_0 | 9 GB |
| FP16 (no quantization) | 16 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.
In practice, Molmo 7B-D fits an 8 GB consumer card at Q4_K_M (5 GB). Stepping up to Q8_0 nearly doubles the footprint to 9 GB, and unquantized FP16 weights take 16 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Molmo 7B-D needs roughly 10 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 10 tokens/sec on entry-level GPUs, on the order of 30 tokens/sec on a mid-range card, and up to 80 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Molmo 7B-D to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for Molmo 7B-D |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Q5_K_M (6 GB used) |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Q8_0 (9 GB used) |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | FP16 (16 GB used) |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | FP16 (16 GB used) |
| 32 GB | RTX 5090 | FP16 (16 GB used) |
Which GPU should you buy to run Molmo 7B-D?
To run Molmo 7B-D locally at Q4, you need ~5 GB of VRAM. The best value for this is a RTX 5060 (8 GB VRAM).
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Published benchmark scores
| Benchmark | Score |
|---|---|
| MMMU | 58.6 |
Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.
To put Molmo 7B-D in context: its MMMU score of 58.6 ranks #6 of the 8 catalog models with a published MMMU result (catalog median 62.8). Scores are author-published and measured under different harnesses, so treat gaps of a few points as noise rather than a verdict.
Strengths
- Pointing capability is rare in open VLMs
- Apache 2.0 across weights and PixMo training data
- Performance lands between GPT-4V and GPT-4o on standard benchmarks
- Transparent human-annotated training set
Limitations
- 4096-token context cap limits multi-turn vision chats
- OCR quality trails Qwen2-VL 7B
- Smaller community ecosystem than mainstream VLMs
Typical workloads
In our catalog grid, Molmo 7B-D is filed under Fully-Open Vision, Grounding/Pointing — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: vision-language work — screenshots, charts, scanned documents.
Note the 4k-token context window — fine for short interactions, limiting for long documents or big retrieval contexts. The Apache 2.0 license is permissive, so shipping it inside a commercial product raises no special legal questions.
Architecture & training
Architecture: Dense · 7B vision · based on Qwen2 7B + OpenAI CLIP encoder
Training: AllenAI PixMo — original human pointing/annotation data, fully open.
The open VLM to choose when you need pointing and grounding under a clean commercial license.
Quick start
ollama run molmoOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does Molmo 7B-D need?
At the recommended Q4_K_M quantization, Molmo 7B-D needs about 5 GB of VRAM. Q8_0 takes 9 GB, and unquantized FP16 weights take 16 GB.
Can Molmo 7B-D run without a GPU?
Yes — with roughly 10 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does Molmo 7B-D support?
Molmo 7B-D supports a 4k-token context window (4,096 tokens).
Can I use Molmo 7B-D commercially?
Yes. Molmo 7B-D is released under Apache 2.0, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is Molmo 7B-D on consumer hardware?
Our compatibility engine estimates on the order of 30 tokens/sec on a mid-range GPU and up to 80 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Molmo 7B-D should I download first?
Start with Q4_K_M (5 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It already fits an 8 GB card at Q5_K_M.