MiniCPM-V 4.5 — VRAM, Speed & Local Setup

Written by Jakub Rusinowski · Last updated September 11, 2026

Model libraryMiniCPM-V → MiniCPM-V 4.5

The 8B that made the series' reputation: an average of 77.0 on OpenCompass, which put it ahead of GPT-4o-latest and Gemini 2.0 Pro and made it the strongest multimodal model under 30B at release. Built on Qwen3-8B with a SigLIP2-400M vision encoder, 32K context, 5.6 GB at Q4_K_M. Code is Apache 2.0; the weights carry OpenBMB's own community licence, which permits free commercial use after registration.

MiniCPM-V 4.5 needs about 6 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache. On Apple Silicon that figure comes out of unified memory.

Specifications

Parameters8 Billion
Context window32,768
ArchitectureSigLIP2-400M encoder + Qwen3-8B decoder
ProviderOpenBMB
LicenceMiniCPM Model License
Specified atQ4_K_M
System RAM16 GB
Record updated2026-09-11

Licence

MiniCPM Model Licensecommercial use permitted. Weights are downloadable and commercial use is permitted, subject to the licence’s acceptable-use terms.

VRAM and Speed by Quantization

Modelled on a reference NVIDIA RTX 4090 (24 GB), with no KV cache (this record has no published architecture). Speed figures are ESTIMATES from the memory-bandwidth roofline described on the methodology page, not benchmarks we ran — rows marked measured come from published or reader-submitted runs. VRAM here includes the KV cache, so it reads higher than the headline figure above, which does not.

QuantWeightsVRAM neededEst. speedFit on 24 GB
Q2_K2.6 GB3.4 GB~154 tok/s (est.)Fits comfortably
Q3_K_M3.4 GB4.2 GB~133 tok/s (est.)Fits comfortably
Q4_K_M4.8 GB5.6 GB~107 tok/s (est.)Fits comfortably
Q5_K_M5.7 GB6.5 GB~95 tok/s (est.)Fits comfortably
Q6_K6.6 GB7.4 GB~86 tok/s (est.)Fits comfortably
Q8_08.5 GB9.3 GB~70 tok/s (est.)Fits comfortably
F1616.0 GB16.8 GB~41 tok/s (est.)Fits comfortably

Want the memory numbers alone, at every quantization level and your own context length? Use the MiniCPM-V 4.5 VRAM calculator.

Buy This HardwareIntel Arc B570 10GB — 10 GB VRAM · 150 W board powerDeploy in the Cloud NowRTX 4090 on RunPod — from $0.34/hr · rate checked 2026-07

or compare on Vast.ai from $0.35/hr (typical low · varies)

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Recommended GPU

The cheapest catalogued GPU that runs MiniCPM-V 4.5 is the Intel Arc B570 (10 GB).

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Intel Arc B570 10GB
10 GB VRAM · 150 W board power
2026 prices are volatile — check the current listing.
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How to Run MiniCPM-V 4.5

Install Ollama, then run:

ollama run minicpm-v4.5

Weights on Hugging Face: openbmb/MiniCPM-V-4_5.

Published Benchmark Scores

Quality scores as published by the model's authors or an independent evaluator — not throughput, and not measured by us.

BenchmarkScoreProvenance
OpenCompass77 / 100 ptsvendor-claimed · https://huggingface.co/openbmb/MiniCPM-V-4_5

Best for: multimodal, vision, document analysis, consumer gpu.

Can I Run MiniCPM-V 4.5 on My GPU?

Other MiniCPM-V Sizes

MiniCPM-V 4.5 — Frequently Asked Questions

How much VRAM does MiniCPM-V 4.5 need?
About 6 GB at Q4_K_M — quantized weights plus framework overhead, before any KV cache. The cache grows with context length and is added on top; the table above folds it in. Apple Silicon counts unified memory toward the same figure.
Does MiniCPM-V 4.5 run on an RTX 4090 (24 GB)?
Yes. MiniCPM-V 4.5 needs about 6 GB at Q4_K_M, inside a 24 GB card, at an estimated 107 tokens/sec.
How do I run MiniCPM-V 4.5 locally?
Install Ollama and run `ollama run minicpm-v4.5`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.
What other sizes does MiniCPM-V come in?
MiniCPM-V 4.6 (2 GB), MiniCPM-V 4.5 (6 GB). Every size shares the family's training and licence; the larger ones score higher and need proportionally more memory.

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