MiniCPM V 4.6 Thinking — Hardware Requirements & GPU Compatibility
Vision
MiniCPM-V 4.6 Thinking is OpenBMB's 1.3-billion-parameter vision-language model, a long chain-of-thought reasoning variant of MiniCPM-V 4.6 built on a SigLIP2-400M vision encoder paired with a small Qwen3.5-0.8B language backbone. It generates an explicit reasoning trace before answering, aimed at multimodal reasoning, math, and OCR-heavy document tasks rather than quick captioning, keeping the same edge-friendly, phone-oriented architecture. Its small size lets it run on a single modest consumer GPU. The model supports a 262,144 token context window. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use, and was published in May 2026. Its distinguishing trait versus base 4.6 is the thinking mode, which trades some latency for better performance on reasoning-heavy visual tasks while reusing the same mixed 4x/16x visual token compression.
109.9K downloads
31 likesSpecifications
- Publisher
- OpenBMB
- Family
- MiniCPM
- Parameters
- 1.3B
- Architecture
- MiniCPMV4_6ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,094
- Release Date
- 2026-05-08
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does MiniCPM V 4.6 Thinking Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 0.9 GB | 7.3 GB | 0.55 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 0.9 GB | 7.3 GB | 0.57 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 1.0 GB | 7.4 GB | 0.63 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 1 GB | 7.4 GB | 0.65 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 1.1 GB | 7.5 GB | 0.78 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 1.3 GB | 7.7 GB | 0.93 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 1.4 GB | 7.8 GB | 1.07 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 1.6 GB | 8.0 GB | 1.30 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 4.6 Thinking?
Q4_K_M · 1.1 GBMiniCPM V 4.6 Thinking (Q4_K_M) requires 1.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ GB is recommended. Using the full 262K context window can add up to 6.4 GB, bringing total usage to 7.5 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run MiniCPM V 4.6 Thinking?
Q4_K_M · 1.1 GB59 devices with unified memory can run MiniCPM V 4.6 Thinking, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download MiniCPM V 4.6 Thinking
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 4.6 Thinking need?
MiniCPM V 4.6 Thinking requires 1.1 GB of VRAM at Q4_K_M, or 3.0 GB at BF16. Full 262K context adds up to 6.4 GB (7.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1.3B × 4.8 bits ÷ 8 = 0.8 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 6.7 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M1.1 GBQ4_K_M + full context7.5 GB- What's the best quantization for MiniCPM V 4.6 Thinking?
For MiniCPM V 4.6 Thinking, Q4_K_M (1.1 GB) offers the best balance of quality and VRAM usage. Q5_0 (1.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 0.7 GB.
VRAM requirement by quantization
IQ2_XXS0.7 GBIQ3_XS0.9 GBQ3_K_L1.0 GBQ4_K_M ★1.1 GBQ5_01.2 GBBF163.0 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run MiniCPM V 4.6 Thinking on a Mac?
MiniCPM V 4.6 Thinking requires at least 0.7 GB at IQ2_XXS, 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 4.6 Thinking locally?
Yes — MiniCPM V 4.6 Thinking can run locally on consumer hardware. At Q4_K_M quantization it needs 1.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is MiniCPM V 4.6 Thinking?
At Q4_K_M, MiniCPM V 4.6 Thinking can reach ~4248 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~580 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 ÷ 1.1 × 0.65 = ~4602 tok/s
Estimated speed at Q4_K_M (1.1 GB)
~4602 tok/s~580 tok/s~4602 tok/s~4248 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 4.6 Thinking?
At Q4_K_M, the download is about 0.78 GB. The full-precision BF16 version is 2.60 GB. The smallest option (IQ2_XXS) is 0.36 GB.
- Which GPUs can run MiniCPM V 4.6 Thinking?
52 consumer GPUs can run MiniCPM V 4.6 Thinking at Q4_K_M (1.1 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
- Which devices can run MiniCPM V 4.6 Thinking?
59 devices with unified memory can run MiniCPM V 4.6 Thinking at Q4_K_M (1.1 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.