Moondream2 — Hardware Requirements & GPU Compatibility
Vision
Moondream2 is a compact 1.9-billion-parameter vision-language model from the Moondream project, published under developer Vikhyat Korrapati's vikhyatk account, designed for lightweight image understanding rather than open-ended chat. It handles image captioning, visual question answering, object detection, and point-based visual grounding, useful for document pipelines and robotics perception where a full-size multimodal model is unnecessary. Its small size means it runs comfortably on modest consumer GPUs and even CPU-only or edge setups. Because Moondream2 focuses on single-image and short-prompt tasks, it does not advertise an extended context window like larger chat models. It is released under the Apache 2.0 license, allowing unrestricted commercial and research use. First published in March 2024, it remains a popular choice for on-device and edge vision applications, with built-in object detection and pointing alongside captioning.
2.0M downloads
1.4K likesSpecifications
- Publisher
- vikhyatk
- Parameters
- 1.9B
- Architecture
- HfMoondream
- Release Date
- 2024-03-04
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Moondream2 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 0.9 GB | — | 0.82 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 1.0 GB | — | 0.94 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 1.3 GB | — | 1.16 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 1.5 GB | — | 1.37 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 1.8 GB | — | 1.59 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 2.1 GB | — | 1.93 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 4.2 GB | — | 3.85 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Moondream2?
Q4_K_M · 1.3 GBMoondream2 (Q4_K_M) requires 1.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Moondream2?
Q4_K_M · 1.3 GB59 devices with unified memory can run Moondream2, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download Moondream2
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does Moondream2 need?
Moondream2 requires 1.3 GB of VRAM at Q4_K_M, or 4.2 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 1.9B × 4.8 bits ÷ 8 = 1.2 GB
KV Cache + Overhead ≈ 0.1 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M1.3 GB- What's the best quantization for Moondream2?
For Moondream2, Q4_K_M (1.3 GB) offers the best balance of quality and VRAM usage. Q5_K_M (1.5 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.9 GB.
VRAM requirement by quantization
Q2_K0.9 GBQ4_K_M ★1.3 GBQ5_K_M1.5 GBQ6_K1.8 GBQ8_02.1 GBBF164.2 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Moondream2 on a Mac?
Moondream2 requires at least 0.9 GB at Q2_K, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Moondream2 locally?
Yes — Moondream2 can run locally on consumer hardware. At Q4_K_M quantization it needs 1.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Moondream2?
At Q4_K_M, Moondream2 can reach ~3780 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~516 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 1.3 × 0.65 = ~4095 tok/s
Estimated speed at Q4_K_M (1.3 GB)
~4095 tok/s~516 tok/s~4095 tok/s~3780 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Moondream2?
At Q4_K_M, the download is about 1.16 GB. The full-precision BF16 version is 3.85 GB. The smallest option (Q2_K) is 0.82 GB.
- Which GPUs can run Moondream2?
52 consumer GPUs can run Moondream2 at Q4_K_M (1.3 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Moondream2?
59 devices with unified memory can run Moondream2 at Q4_K_M (1.3 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.