v2.2 · 2026-06-12

VISION / MULTIMODAL MODEL — RUNTIME REQUIREMENTS

·SigLIP2-400M vision encoder with unified 3D-Resampler; official GGUF supports image-text workflows through MiniCPM-V-capable llama.cpp/Ollama runtimes.

·Transformers usage may require `trust_remote_code`; local GGUF use requires MiniCPM-V-capable runtime support.

·Video and long-context image use can substantially increase memory through KV cache and visual tokens.

·RAM and VRAM estimates are derived from GGUF/Ollama package size plus conservative overhead; KV cache, image/video inputs, batch size, GPU offload and runtime support can materially change memory needs.

MiniCPM-V 4.5 8B

minicpm-v-4-5-8b

VISION-LANGUAGE

MiniCPM-V 4.5 8B is a 8B vision language model profile for local AI planning. This page records provider, license, context, quantization, hardware-fit estimates, setup hints, source links, and caveats so readers do not choose a model by name alone.

PROVIDER

OpenBMB

FAMILY

MiniCPM-V

MODEL TYPE

vision-language

PARAMETERS

MODALITIES

text, image, video

ARCHITECTURE

MiniCPM-V 4.5 multimodal architecture built on a Qwen3-8B language model, SigLIP2-400M vision encoder, and unified 3D-Resampler.

CONTEXT WINDOW

40,960 tokens

TRAINING TOKENS

—

RELEASE DATE

2025-09-16

LICENSE & LINKS

Apache-2.0license text

MODEL CARD

huggingface.co/openbmb/MiniCPM-V-4_5

HUGGING FACE

openbmb/MiniCPM-V-4_5

GGUF REPOSITORIES

openbmb/MiniCPM-V-4_5-gguf official

Official OpenBMB GGUF repository with Q4_K_M, Q5_K_M, Q8_0 and llama.cpp server examples.

SETUP HINTS

OLLAMA

Run `ollama run openbmb/minicpm-v4.5:q4_K_M`; prefer the packaged runtime for vision/video support.

LM STUDIO

Use only if the installed LM Studio build supports MiniCPM-V GGUF vision loading; otherwise prefer Ollama or llama.cpp server as documented by OpenBMB.

LLAMA.CPP

Use the official GGUF repository's pattern, for example `llama-server -hf openbmb/MiniCPM-V-4_5-gguf:Q4_K_M`, with a llama.cpp build that supports MiniCPM-V.

QUANTIZATION FILE SIZES (GGUF)

Q4_K_M

5.03 GB

Q5_K_M

5.85 GB

Q8_0

8.71 GB

Quantization note: Q4_K_M for local image and video experiments; Q5_K_M when memory allows.

RAM / VRAM ESTIMATES

MIN RAM

12 GB

COMFORTABLE RAM

24 GB

MIN VRAM

8 GB

COMFORTABLE VRAM

12 GB

These are conservative local-inference estimates, not official hardware requirements.

HARDWARE FIT

CPU only (no GPU)

limited

8 GB RAM

16 GB RAM

limited

32 GB RAM

yes

8 GB VRAM

limited

12 GB VRAM

yes

24 GB VRAM

yes

Apple Silicon (unified memory)

yes

Hardware fit values are conservative local-inference estimates based on GGUF size plus runtime and KV-cache overhead. Actual requirements depend on context length, quantization, runtime, GPU offload, and other running apps.

BEST FOR

·Compact local vision-language chat

·OCR-style document and image parsing experiments

·Video and multi-image understanding demos

·Multilingual multimodal use cases

·Edge or workstation visual assistant prototypes

AVOID IF

·You only have 8GB system RAM

·You need a plain Llama-compatible loader without MiniCPM-V support

·You need a verified structured tool-calling model

·You need official fixed memory requirements rather than estimates

CAVEATS