MiniCPM-V 2.6 8B
By OpenBMB · China
Updated 2026-07-13
Overview
OpenBMB's 8B vision-language model pairing SigLIP and Qwen2, scoring 65.2 on OpenCompass and beating GPT-4o on OCRBench among sub-25B models.
When to pick this model
- OCR and document extraction at high resolution
- Multi-image and video understanding on a single GPU
- VLM workloads needing 32k context
- Replacing GPT-4V for screenshot and form parsing
- Mobile and consumer-grade inference of multimodal apps
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 5.5 GB |
| Q5_K_M | 7 GB |
| Q8_0 | 10 GB |
| FP16 (no quantization) | 18 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, MiniCPM-V 2.6 8B fits an 8 GB consumer card at Q4_K_M (5.5 GB). Stepping up to Q8_0 nearly doubles the footprint to 10 GB, and unquantized FP16 weights take 18 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, MiniCPM-V 2.6 8B needs roughly 12 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 MiniCPM-V 2.6 8B 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 MiniCPM-V 2.6 8B |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Q5_K_M (7 GB used) |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Q8_0 (10 GB used) |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Q8_0 (10 GB used) |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | FP16 (18 GB used) |
| 32 GB | RTX 5090 | FP16 (18 GB used) |
Which GPU should you buy to run MiniCPM-V 2.6 8B?
To run MiniCPM-V 2.6 8B locally at Q4, you need ~5.5 GB of VRAM. The best value for this is a RTX 5060 (8 GB VRAM).
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Published benchmark scores
| Benchmark | Score |
|---|---|
| OpenCompass | 65.2 |
Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.
Strengths
- Beats GPT-4o on OCRBench in the sub-25B class
- OpenCompass 65.2 matches much larger VLMs
- Handles 1.8MP inputs without aggressive downsampling
- Native multi-image and video reasoning
- Free aspect-ratio handling avoids letterboxing artifacts
Limitations
- MiniCPM Model License requires registration for commercial use
- Smaller community than Qwen2-VL or Llama-class VLMs
- Tooling support varies across inference backends
Typical workloads
In our catalog grid, MiniCPM-V 2.6 8B is filed under OCR, Video, Multi-image — 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 31k-token context window — fine for short interactions, limiting for long documents or big retrieval contexts. It ships under the MiniCPM Model License license — commercial use is generally possible but read the specific terms before embedding it in a product.
Architecture & training
Architecture: VLM 8B · SigLIP-400M + Qwen2-7B
Training: Multi-image, video, free aspect ratio.
The OCR champion among compact open VLMs — the right call when document fidelity beats pure chat quality.
Quick start
ollama run minicpm-v:8bOr 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 MiniCPM-V 2.6 8B need?
At the recommended Q4_K_M quantization, MiniCPM-V 2.6 8B needs about 5.5 GB of VRAM. Q8_0 takes 10 GB, and unquantized FP16 weights take 18 GB.
Can MiniCPM-V 2.6 8B run without a GPU?
Yes — with roughly 12 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 MiniCPM-V 2.6 8B support?
MiniCPM-V 2.6 8B supports a 31k-token context window (32,000 tokens).
Can I use MiniCPM-V 2.6 8B commercially?
MiniCPM-V 2.6 8B ships under the MiniCPM Model License license. Commercial use is generally permitted subject to its terms — review the license text before shipping a product.
How fast is MiniCPM-V 2.6 8B 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 MiniCPM-V 2.6 8B should I download first?
Start with Q4_K_M (5.5 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.