How much VRAM does Phi-4 14B need?

Phi-4 14B 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 14B on a consumer GPU?

Yes, Phi-4 14B can run on consumer GPUs. At Q4_K_M quantization with moderate context length, it fits on a 24 GB GPU. For best performance, use a 24 GB or larger GPU with at least 8 GB VRAM for reasonable context lengths.

What is Phi-4 14B's context window?

Phi-4 14B 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 14B?

For Phi-4 14B, start with Q4_K_M (~0.0 GB) if VRAM-limited. Q8_0 offers better quality at ~2× the VRAM. If you have enough VRAM, Q6_K (~1.5× Q4 VRAM) provides a good balance. For models this large, Q3_K_M may be necessary to fit on consumer hardware.

How does Phi-4 14B compare to similar models?

Phi-4 14B is a 14.7B 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 14B

14.7B

Parameters

16K

Max Context

Dense

Architecture

Dec 12, 2024

Released

Text

Modality

About Phi-4 14B

Phi-4 14B is Microsoft's math and reasoning specialist. Despite its modest 14.7B parameter count, it achieves performance competitive with 70B models on mathematical reasoning, logic puzzles, and structured problem-solving. The MIT license makes it safe for commercial use. At ~8 GB VRAM at Q4_K_M, it fits on 12 GB GPUs. The trade-off: it is less strong on creative writing, general chat, and world knowledge compared to general-purpose models of similar size. Best used as a specialized reasoning tool alongside a general-purpose model.

MathReasoningSTEMLogicCommercial

Technical Specifications

Total Parameters14.7B

ArchitectureDense

Attention TypeGQA (Grouped Query Attention)

Hidden Dimensiond = 5,120

Transformer Layers40

Attention Heads40

KV Headsn_kv = 10

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	7.79Consumer GPU	10.72Consumer GPU
Q8_01.00 B/W ~100% of FP16	15.39Consumer GPU	18.32Consumer GPU
F162.00 B/W Reference	30.59Datacenter GPU	33.52Datacenter GPU

Fits 24 GB consumer GPU

Fits 80 GB datacenter GPU

Requires cluster / multi-GPU

Other Phi Models

View All

Phi-4-mini 3.8B

Params

3.8B

Layers

Context

16K

Phi-4-multimodal 5.6B

Params

5.6B

Layers

Context

16K

Find the right GPU for Phi-4 14B

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