How much VRAM does MiniCPM 4 8B need?

MiniCPM 4 8B requires approximately 0.0 GB VRAM at Q4_K_M quantization with 1K context and 10% overhead. At Q8_0 it needs ~0.0 GB, and at FP16 ~0.0 GB. Use the PCPARTGUIDE VRAM Calculator for exact requirements at any context length and overhead setting.

Can I run MiniCPM 4 8B on a consumer GPU?

Yes, MiniCPM 4 8B can run on consumer GPUs. At Q4_K_M quantization with moderate context length, it fits on a 12 GB GPU. For best performance, use a 12 GB or larger GPU with at least 8 GB VRAM for reasonable context lengths.

What is MiniCPM 4 8B's context window?

MiniCPM 4 8B supports a maximum context window of 32,768 tokens. The default context is 4,096 tokens. Larger contexts require proportionally more VRAM for the KV cache — use the VRAM Calculator to estimate exact requirements at your target context length.

What quantization level should I use for MiniCPM 4 8B?

For MiniCPM 4 8B, Q4_K_M is the recommended quantization — it provides an excellent quality-to-VRAM ratio at ~0.0 GB. Q8_0 offers slightly better quality at ~2× the VRAM. Q4_K_M is the sweet spot for most users.

How does MiniCPM 4 8B compare to similar models?

MiniCPM 4 8B is a 8.0B Dense model from the MiniCPM family. Browse other MiniCPM models on the PCPARTGUIDE directory for comparable alternatives with different parameter counts and VRAM requirements. Use the Model Transparency scores to assess documentation quality and reproducibility.

MiniCPMDenseOpen Weights

MiniCPM 4 8B

MiniCPM 4 8B is a dense transformer language model from the MiniCPM family, containing 8B parameters across 32 layers. It supports up to 33K tokens of context with a hidden dimension of 4096 and 8 KV heads for efficient grouped-query attent…

8.0B

Parameters

32K

Max Context

Dense

Architecture

—

Released

Text

Modality

About MiniCPM 4 8B

AgenticOn-Device

Technical Specifications

Total Parameters8.0B

ArchitectureDense

Attention TypeGQA (Grouped Query Attention)

Hidden Dimensiond = 4,096

Transformer Layers32

Attention Heads32

KV Headsn_kv = 8

Head Dimensiond_head = 128

Activation FunctionSwiGLU

NormalizationRMSNorm

Position EmbeddingRoPE

System Requirements

Estimated VRAM at 10% overhead for different quantization methods and context sizes.

Quantization	1K ctx	32K ctx
Q4_K_M0.50 B/W ~97% of FP16	4.26Consumer GPU	8.14Consumer GPU
Q8_01.00 B/W ~100% of FP16	8.40Consumer GPU	12.27Consumer GPU
F162.00 B/W Reference	16.67Consumer GPU	20.54Consumer GPU

Fits 24 GB consumer GPU

Fits 80 GB datacenter GPU

Requires cluster / multi-GPU

Find the right GPU for MiniCPM 4 8B

Use the interactive VRAM Calculator to see exactly how much memory you need at any quantization level, context length, and overhead setting.

Open VRAM Calculator Best GPU Guide VRAM Guide