MiniCPM V 2 6 — Hardware Requirements & GPU Compatibility
Vision
MiniCPM-V 2.6 is OpenBMB's 8.1-billion-parameter multimodal model from the MiniCPM-V series, aimed at single-image, multi-image and video understanding. Its successor's card describes MiniCPM-V 4.0 as inheriting the single-image, multi-image and video performance of this model, and lists it at an OpenCompass score of 65.2. It pairs a SigLIP-400M vision encoder with a Qwen2-7B language model. At 8 billion parameters it runs on a single consumer GPU once quantized. Weights are released under OpenBMB's MiniCPM Model License with the code under Apache 2.0, so read the model terms before commercial use. It was published in August 2024 and has been superseded within the series by the 4B MiniCPM-V 4.0, which the card for that model reports as scoring higher on OpenCompass at about half the size.
35.6K downloads
1.1K likesSpecifications
- Publisher
- OpenBMB
- Family
- MiniCPM
- Parameters
- 8.1B
- Release Date
- 2024-08-04
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HuggingFace
How Much VRAM Does MiniCPM V 2 6 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 3.8 GB | — | 3.44 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 3.9 GB | — | 3.54 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 4.3 GB | — | 3.95 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 4.5 GB | — | 4.05 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 5.3 GB | — | 4.86 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 6.3 GB | — | 5.77 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 7.3 GB | — | 6.68 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 8.9 GB | — | 8.10 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 2 6?
Q4_K_M · 5.3 GBMiniCPM V 2 6 (Q4_K_M) requires 5.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ GB is recommended. 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 2 6?
Q4_K_M · 5.3 GB58 devices with unified memory can run MiniCPM V 2 6, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomWhere to Download MiniCPM V 2 6
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 2 6 need?
MiniCPM V 2 6 requires 5.3 GB of VRAM at Q4_K_M, or 17.8 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 8.1B × 4.8 bits ÷ 8 = 4.9 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M5.3 GB- What's the best quantization for MiniCPM V 2 6?
For MiniCPM V 2 6, Q4_K_M (5.3 GB) offers the best balance of quality and VRAM usage. Q4_K_L (5.5 GB) provides better quality if you have the VRAM. The smallest option is IQ2_M at 3.0 GB.
VRAM requirement by quantization
IQ2_M3.0 GBQ3_K_M4.3 GBQ4_K_S5.0 GBQ4_K_M ★5.3 GBQ5_16.1 GBBF1617.8 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run MiniCPM V 2 6 on a Mac?
MiniCPM V 2 6 requires at least 3.0 GB at IQ2_M, 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 2 6 locally?
Yes — MiniCPM V 2 6 can run locally on consumer hardware. At Q4_K_M quantization it needs 5.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is MiniCPM V 2 6?
At Q4_K_M, MiniCPM V 2 6 can reach ~897 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~123 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 ÷ 5.3 × 0.65 = ~972 tok/s
Estimated speed at Q4_K_M (5.3 GB)
~972 tok/s~123 tok/s~972 tok/s~897 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 2 6?
At Q4_K_M, the download is about 4.86 GB. The full-precision BF16 version is 16.20 GB. The smallest option (IQ2_M) is 2.73 GB.
- Which GPUs can run MiniCPM V 2 6?
52 consumer GPUs can run MiniCPM V 2 6 at Q4_K_M (5.3 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 2 6?
59 devices with unified memory can run MiniCPM V 2 6 at Q4_K_M (5.3 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.