Family MiniCPM · 8B parameters

MiniCPM-V 2.6 8B

8B VLM (SigLIP + Qwen2). OpenCompass 65.2. Beats GPT-4o on OCRBench <25B. Multi-image/video.

🇨🇳 OpenBMB·License MiniCPM Model License·Context 31.25k tokens·Output August 2024·Fits within the 128 GB of the GIGABYTE AI TOP ATOM← Catalog

01What it can do

Strengths
  • Beats GPT-4o on OCRBench <25B
  • OpenCompass 65.2
  • 1.8MP input
Limitations to know
  • —MiniCPM license (commercial use with registration)
Architecture
VLM 8B · SigLIP-400M + Qwen2-7B
Training
Multiple images, video, unrestricted aspect ratio.
Ideal for
OCRVideoMulti-image

05Install

Install Ollama for your OS. Check the model and its quantization before downloading. Start with 4096 tokens of context, then check placement with ollama ps. A command below is not proof that a test was run on your machine.

$ollama run minicpm-v:8b
⚠
First download: between 2 and 40 GB depending on the selected quantization. Plan for sufficient disk space; a stable connection is recommended. Subsequent launches are instant.

02Required memory

Approximate GPU VRAM required to run this model, including 4k tokens of context overhead. For a longer context, add ~1 GB per 8k-token increment.

Q4_K_M
The lightest, ~5% loss
5.5 GB
Q5_K_M
Good quality/size compromise
7 GB
Q8_0
Nearly indistinguishable from FP16
10 GB
FP16
Full precision — server use
18 GB
Fallback CPU · If you don't have a GPU, allow 12 GB of RAM minimum to run this model at reduced speed.

What hardware do you need for MiniCPM-V 2.6 8B?

To run MiniCPM-V 2.6 8B locally with Q4 quantization, you need about 5.5 GB of VRAM. An option to compare: RTX 5060 Ti 16GB (ASUS Prime) — leave some headroom for the system and context; check engine compatibility with the GPU.

Current offer: RTX 5060 Ti 16GB (ASUS Prime)
AmazonSee price →

Affiliate links — commission possible at no extra cost to you; independent recommendations. As an Amazon Associate, BestLLMfor earns from qualifying purchases.

On the go: MiniCPM-V 2.6 8B also runs on a RTX laptop PC (16 GB of VRAM) →

This model in your private ChatGPT, without the cloud

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03Expected speed

Tokens generated per second in Q4_K_M, 4k context. Beyond 20 t/s, reading is comfortable. Below 10 t/s, that's just for testing.

Entry-level
~10t/s
GTX 1650, RX 6600, MBA M2 8GB
Mid-range
~30t/s
RTX 4060, 4070, MBP M3 Pro
High-end
~80t/s
RTX 4090, M4 Max, Radeon 7900

04Public benchmarks

Scores reproduced from model cards or MMLU-Pro / community sources. Unit: % correct answers.

OpenCompass
65.2