How much VRAM does Mistral Small 3.1 24B need?

Mistral Small 3.1 24B 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 Mistral Small 3.1 24B on a consumer GPU?

Yes, Mistral Small 3.1 24B 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 Mistral Small 3.1 24B's context window?

Mistral Small 3.1 24B supports a maximum context window of 131,072 tokens. The default context is 8,192 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 Mistral Small 3.1 24B?

For Mistral Small 3.1 24B, 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 Mistral Small 3.1 24B compare to similar models?

Mistral Small 3.1 24B is a 24.0B 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

Mistral Small 3.1 24B

24.0B

Parameters

128K

Max Context

Dense

Architecture

Mar 17, 2025

Released

Text + Vision

Modality

About Mistral Small 3.1 24B

Mistral Small 3.1 24B is one of the best models you can run on a single 24 GB consumer GPU. A dense 24B parameter model that fits entirely on an RTX 3090/4090 at Q4_K_M (~13 GB) with room for long context. Includes vision encoder, function calling, and strong multilingual performance. Apache 2.0 licensed. This is the go-to recommendation for anyone with a 24 GB GPU who wants maximum quality without offloading.

General PurposeCodeVisionMultilingualCommercial

Technical Specifications

Total Parameters24.0B

ArchitectureDense

Attention TypeGQA (Grouped Query Attention)

Hidden Dimensiond = 6,144

Transformer Layers56

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	128K ctx
Q4_K_M0.50 B/W ~97% of FP16	12.62Consumer GPU	40.41Datacenter GPU
Q8_01.00 B/W ~100% of FP16	25.03Datacenter GPU	52.81Datacenter GPU
F162.00 B/W Reference	49.84Datacenter GPU	77.62Datacenter 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 Mistral Small 3.1 24B

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