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Last updated: 2026-10-28

[Models](https://getrigbase.com/models/) / MiniCPM-V 4.7

# Best home rig for MiniCPM-V 4.7

Quick answer

**How much VRAM does MiniCPM-V 4.7 need?**
The weights are about 21.4 GB at Q4_K_M (35B params × 4.89 bits ÷ 8, before context/KV cache). Add memory for context (KV cache) and the runtime. The smallest-memory setup with a measured result here, fully in memory, is 2x RTX 3090 (48 GB) at 198.7 tok/s.

**What is the fastest measured hardware for MiniCPM-V 4.7?**
2x RTX 3090: 198.7 tok/s generation (vLLM TP2, 1 stream, INT4 + DSpark), from https://github.com/tonyd2wild/MiniCPM-V-4.7-35B-A3B-2x-RTX-3090.

Last updated: 28 Oct 2026

Current model 35B total · 3B active 1 measured result  [model info ↗](https://huggingface.co/openbmb/MiniCPM-V-4.7)

[Will it fit my GPU?](https://getrigbase.com/can-it-run/) [Best value GPU per euro →](https://getrigbase.com/guides/best-value-gpu-local-llm-europe/)

Real, measured generation speeds for MiniCPM-V 4.7 on home hardware, fastest first. Different quants/engines aren't directly comparable.

| Build | Quant | Engine | tok/s | Prompt processing (tok/s) | Cost | Source |
|---|---|---|---|---|---|---|
| [2x RTX 3090: MiniCPM-V 4.7 INT4 on vLLM TP2, 198.7 tok/s prose (Tech2wild)](https://getrigbase.com/builds/tony-2x-rtx-3090-minicpm-v-4-7-35b-a3b-2x-rtx-3090/) | INT4 + DSpark | vLLM TP2, 1 stream | **198.7** ⚡ | – |  | [github.com ↗](https://github.com/tonyd2wild/MiniCPM-V-4.7-35B-A3B-2x-RTX-3090) |

Prompt processing = prefill speed reported by the source, @ prompt length. * est. TTFT @8k is calculated from measured prefill, not measured. [Why it matters](https://getrigbase.com/can-it-run/#prefill).
Ad · this space is available **Sponsor this spot: reach people building local AI rigs.** One clearly-labeled slot per page, no tracking scripts.  [Sponsor this spot →](https://getrigbase.com/advertise/#inquiry)

**Rent before you buy:** try this model on a cloud GPU for a few dollars first: [Runpod](https://runpod.io?ref=77qcpl2c) (referral link; sign up with Google to get the new-user bonus)

## Memory math: how much VRAM / RAM does MiniCPM-V 4.7 need?

MiniCPM-V 4.7 has **35B parameters**, about **3B active** per token ([source](https://huggingface.co/openbmb/MiniCPM-V-4.7): OpenBMB MiniCPM-V 4.7 MoE vision (35B-A3B); int4 pack used in Tech2wild recipe). Weight size = parameters × bits-per-weight ÷ 8, before context (KV cache) and runtime overhead.

| Format | Bits/weight | Weights | Smallest common memory size that holds the weights alone |
|---|---|---|---|
| Q8_0 | 8.50 | 37.2 GB | 48 GB |
| Q6_K | 6.56 | 28.7 GB | 32 GB |
| Q5_K_M | 5.70 | 25.0 GB | 32 GB |
| Q4_K_M | 4.89 | **21.4 GB** | 24 GB |
| Q3_K_M | 4.00 | 17.5 GB | 24 GB |
| Q2_K | 3.16 | 13.8 GB | 16 GB |

Bits-per-weight for GGUF quants are llama.cpp's own figures ([llama.cpp quantize README](https://github.com/ggml-org/llama.cpp/blob/master/tools/quantize/README.md)). "As measured" rows apply the source's stated bits-per-weight to the parameter count; real files differ slightly because some layers stay at higher precision. Try other combinations in the [VRAM calculator](https://getrigbase.com/can-it-run/).

**Why it can still run on smaller GPUs:** it is a mixture-of-experts model, so only about 3B of its 35B parameters are used for each generated token. Runtimes such as llama.cpp (--n-cpu-moe), ExLlamaV3 or Project Maya can keep most expert weights in system RAM and only the shared layers and hot experts in VRAM. Then you need **VRAM + system RAM ≥ weights**, and speed is limited mainly by RAM bandwidth.

## FAQ

### How much VRAM does MiniCPM-V 4.7 need?

The weights are about 21.4 GB at Q4_K_M (35B params × 4.89 bits ÷ 8, before context/KV cache). Add memory for context (KV cache) and the runtime. The smallest-memory setup with a measured result here, fully in memory, is 2x RTX 3090 (48 GB) at 198.7 tok/s.

### What is the fastest measured hardware for MiniCPM-V 4.7?

2x RTX 3090: 198.7 tok/s generation (vLLM TP2, 1 stream, INT4 + DSpark), from https://github.com/tonyd2wild/MiniCPM-V-4.7-35B-A3B-2x-RTX-3090.

### Which GPUs have measured results for MiniCPM-V 4.7?

2x RTX 3090.

**Can it run on…** [2x RTX 3090](https://getrigbase.com/can-i-run/minicpm-v-4-7-on-2x-rtx-3090/)

Related: [what GPU do you need for 2026 models](https://getrigbase.com/guides/what-gpu-for-qwen3-6-gemma-4-gpt-oss/) · [all builds](https://getrigbase.com/builds/)