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Local LLM Guide

Model

MiniCPM-V 4.5 8B

Start with a supported compressed build and short context. 24GB RAM estimate. Best use: Compact local vision-language chat

8BVision language

Best use

Compact local vision-language chat · OCR-style document and image parsing experiments

Memory planning

24GB RAM estimate

Expected pace

Depends on your hardware, app, and conversation length

License

Apache-2.0; read the model’s terms before use

Who should use this model?

  • 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

Choose something else when

  • You only have 8GB system RAM
  • You need a plain Llama-compatible loader without MiniCPM-V support
  • You need a supported structured tool-calling model
  • You need official fixed memory requirements rather than estimates

What hardware makes sense?

The download is only part of the memory needed. Leave room for the app, the input it processes, and other open programs. For chat models, context includes the instructions, conversation, and answer. Longer context needs more memory.

CPU only
Memory or response time may limit use. Try a smaller model first.
8GB system memory
Use a smaller model for this memory limit.
16GB system memory
Memory or response time may limit use. Try a smaller model first.
32GB system memory
A possible starting point with a compressed model and short conversation; check the exact file size.
8GB GPU memory
Memory or response time may limit use. Try a smaller model first.
12GB GPU memory
A possible starting point with a compressed model and short conversation; check the exact file size.
24GB GPU memory
A possible starting point with a compressed model and short conversation; check the exact file size.
Apple unified memory
A possible starting point with a compressed model and short conversation; check the exact file size.

Model facts that change the decision

Parameters
8B
Input
Text · Image · Video
Published context limit
40,960 tokens
Model format
Hugging Face Transformers · GGUF · Ollama · llama.cpp
Suggested compression
Q4_K_M for local image and video experiments; Q5_K_M when memory allows.
Typical Q4 file
5.03GB before runtime and context overhead
Languages
multilingual; 30+ languages

Using image input

This model needs an app or library that supports its image-processing components. Follow the exact model’s instructions for preparing image inputs. Some GGUF downloads also need a matching projector file; loading the text model alone does not establish image support.

First setup

Ollama

ollama run openbmb/minicpm-v4.5:q4_K_M

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.

Conditions that change the answer

  • Try a representative prompt in the language you need before choosing this model.
  • 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 conversation memory and visual tokens.
  • RAM and VRAM estimates are derived from GGUF/Ollama package size plus conservative overhead; conversation memory, image/video inputs, batch size, GPU offload and runtime support can materially change memory needs.

Read the controlling license text →