How much VRAM does Phi-4-multimodal 5.6B need?

Phi-4-multimodal 5.6B 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 Phi-4-multimodal 5.6B on a consumer GPU?

Yes, Phi-4-multimodal 5.6B 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 Phi-4-multimodal 5.6B's context window?

Phi-4-multimodal 5.6B supports a maximum context window of 16,384 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 Phi-4-multimodal 5.6B?

For Phi-4-multimodal 5.6B, 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 Phi-4-multimodal 5.6B compare to similar models?

Phi-4-multimodal 5.6B is a 5.6B Dense model from the Phi family. Browse other Phi 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.

PhiDenseMIT

Phi-4-multimodal 5.6B

Phi-4-multimodal 5.6B is a dense transformer language model from the Phi family, containing 5.6B parameters across 36 layers. It supports up to 16K tokens of context with a hidden dimension of 3584 and 7 KV heads for efficient grouped-query…

5.6B

Parameters

16K

Max Context

Dense

Architecture

—

Released

Text + Audio + Vision

Modality

About Phi-4-multimodal 5.6B

General PurposeChat

Technical Specifications

Total Parameters5.6B

ArchitectureDense

Attention TypeGQA (Grouped Query Attention)

Hidden Dimensiond = 3,584

Transformer Layers36

Attention Heads28

KV Headsn_kv = 7

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	16K ctx
Q4_K_M0.50 B/W ~97% of FP16	3.02Consumer GPU	4.86Consumer GPU
Q8_01.00 B/W ~100% of FP16	5.91Consumer GPU	7.76Consumer GPU
F162.00 B/W Reference	11.70Consumer GPU	13.55Consumer GPU

Fits 24 GB consumer GPU

Fits 80 GB datacenter GPU

Requires cluster / multi-GPU

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Params

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Layers

Context

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Params

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Layers

Context

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Find the right GPU for Phi-4-multimodal 5.6B

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

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