MiniCPM V 4 5 — Hardware Requirements & GPU Compatibility
Vision
MiniCPM-V 4.5 is an 8.7-billion-parameter vision-language model from OpenBMB, combining a Qwen3-8B language backbone with a SigLIP2 vision encoder. It is designed to run efficiently on modest hardware, including phones and laptops, while handling image, multi-image, and video understanding alongside text chat; a unified resampler compresses video frames heavily so longer clips don't demand proportionally more compute. At under 9 billion parameters, it fits comfortably on a single mainstream consumer GPU once quantized. It supports a 40,960 token context window, sufficient for long documents or multi-turn multimodal conversations. It is released under the Apache 2.0 license, allowing unrestricted commercial and research use. Published in August 2025, it distinguishes itself with a switchable "fast" and "deep" thinking mode, trading response speed for more deliberate reasoning on harder problems.
157.0K downloads
1.1K likesSpecifications
- Publisher
- OpenBMB
- Family
- MiniCPM
- Parameters
- 8.7B
- Architecture
- MiniCPMV
- Context Length
- 40,960 tokens
- Vocabulary Size
- 151,748
- Release Date
- 2025-08-24
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does MiniCPM V 4 5 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 4.3 GB | 10.0 GB | 3.70 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 4.4 GB | 10.1 GB | 3.80 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 4.8 GB | 10.6 GB | 4.24 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 5.0 GB | 10.7 GB | 4.35 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 5.8 GB | 11.6 GB | 5.22 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 6.8 GB | 12.5 GB | 6.20 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 7.8 GB | 13.5 GB | 7.17 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 9.3 GB | 15.0 GB | 8.70 GB | 8-bit quantization, near-lossless |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run MiniCPM V 4 5?
Q4_K_M · 5.8 GBMiniCPM V 4 5 (Q4_K_M) requires 5.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. Using the full 41K context window can add up to 5.7 GB, bringing total usage to 11.6 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run MiniCPM V 4 5?
Q4_K_M · 5.8 GB58 devices with unified memory can run MiniCPM V 4 5, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomWhere to Download MiniCPM V 4 5
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does MiniCPM V 4 5 need?
MiniCPM V 4 5 requires 5.8 GB of VRAM at Q4_K_M, or 18.0 GB at BF16. Full 41K context adds up to 5.7 GB (11.6 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8.7B × 4.8 bits ÷ 8 = 5.2 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 6.4 GB (at full 41K context)
VRAM usage by quantization
Q4_K_M5.8 GBQ4_K_M + full context11.6 GB- What's the best quantization for MiniCPM V 4 5?
For MiniCPM V 4 5, Q4_K_M (5.8 GB) offers the best balance of quality and VRAM usage. Q5_0 (6.0 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 4.3 GB.
VRAM requirement by quantization
Q2_K4.3 GBQ3_K_L5.1 GBQ4_K_M ★5.8 GBQ5_06.0 GBQ5_K_M6.8 GBBF1618.0 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run MiniCPM V 4 5 on a Mac?
MiniCPM V 4 5 requires at least 4.3 GB at Q2_K, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run MiniCPM V 4 5 locally?
Yes — MiniCPM V 4 5 can run locally on consumer hardware. At Q4_K_M quantization it needs 5.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is MiniCPM V 4 5?
At Q4_K_M, MiniCPM V 4 5 can reach ~825 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~113 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 5.8 × 0.65 = ~894 tok/s
Estimated speed at Q4_K_M (5.8 GB)
~894 tok/s~113 tok/s~894 tok/s~825 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of MiniCPM V 4 5?
At Q4_K_M, the download is about 5.22 GB. The full-precision BF16 version is 17.39 GB. The smallest option (Q2_K) is 3.70 GB.
- Which GPUs can run MiniCPM V 4 5?
52 consumer GPUs can run MiniCPM V 4 5 at Q4_K_M (5.8 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 40 GPUs have plenty of headroom for comfortable inference.
- Which devices can run MiniCPM V 4 5?
59 devices with unified memory can run MiniCPM V 4 5 at Q4_K_M (5.8 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.