How much VRAM does Ministral 3 14B need?

Ministral 3 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 Ministral 3 14B on a consumer GPU?

Yes, Ministral 3 14B 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 16 GB or larger GPU with at least 8 GB VRAM for reasonable context lengths.

What is Ministral 3 14B's context window?

Ministral 3 14B supports a maximum context window of 262,144 tokens. The default context is 16,384 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 Ministral 3 14B?

For Ministral 3 14B, 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 Ministral 3 14B compare to similar models?

Ministral 3 14B is a 14.0B Dense model from the Mistral family. Browse other Mistral 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.

MistralDenseApache 2.0

Ministral 3 14B

14.0B

Parameters

256K

Max Context

Dense

Architecture

Sep 16, 2025

Released

Text + Vision

Modality

About Ministral 3 14B

Ministral 3 14B is the largest of the Ministral 3 cascade-distilled family, derived from the Mistral Small 3.1 24B teacher. It includes a vision encoder and delivers strong laptop-class coding and general performance. At ~8 GB VRAM at Q4_K_M it fits on 12 GB GPUs and runs fast on 16 GB. Apache 2.0 licensed with 262K context support. A strong choice for users who want the best laptop-class model with vision capabilities.

General PurposeCodeVisionLaptopCommercial

Technical Specifications

Total Parameters14.0B

ArchitectureDense

Attention TypeGQA (Grouped Query Attention)

Hidden Dimensiond = 5,120

Transformer Layers40

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	195K ctx	256K ctx
Q4_K_M0.50 B/W ~97% of FP16	7.39Consumer GPU	37.75Datacenter GPU	47.24Datacenter GPU
Q8_01.00 B/W ~100% of FP16	14.63Consumer GPU	44.99Datacenter GPU	54.47Datacenter GPU
F162.00 B/W Reference	29.10Datacenter GPU	59.46Datacenter GPU	68.95Datacenter GPU

Params

675.0B

Layers

Context

256K

41.0B active

Mistral 7B v0.3

Params

7.3B

Layers

Context

32K

Find the right GPU for Ministral 3 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