ApX logoApX logo

Devstral 2 123B Instruct

Parameters

-

Context Length

262K

Modality

Text

Architecture

Dense

License

Modified MIT

Release Date

1 Dec 2025

Knowledge Cutoff

-

System Requirements

VRAM requirements for different quantization methods and context sizes

1,024 tokens

1.89 GB VRAM

Consumer

1x RTX 4090

24GB VRAM

Datacenter

1x NVIDIA A100

80GB VRAM

Apple Silicon

1x Apple M3 Max

128GB VRAM

262,144 tokens

100.71 GB VRAM

Consumer

5x RTX 4090

24GB VRAM

Datacenter

2x NVIDIA A100

80GB VRAM

Apple Silicon

1x Apple M3 Max

128GB VRAM

Architecture Diagram

Input TokensToken EmbeddingPosition: AbsoluteHidden: 12.3k · Context: 262K · Vocab: 131.1kx 88 layersRMSNormPre-AttentionMulti-Head Attention96Q / 8KV headsHead dim: 128+RMSNormPre-FFNFeed-Forward NetworkSwiGLUIntermediate: 28.7k+Final RMSNormOutput Logits

Evaluation Benchmarks

No evaluation benchmarks for Devstral 2 123B Instruct available.

Rankings

Overall Rank

-

Coding Rank

-

About Devstral 2 123B Instruct

Devstral 2 123B Instruct is an agentic dense 123B parameter software engineering model from Mistral AI. It is optimized for multi-file codebase exploration, editing, and powering coding agents, with a 262k context window.

Technical Specifications

Attention

Attention Structure

Multi-Head Attention

Attention Heads

96

Key-Value Heads

8

Attention Head Dimension

128

Position Embedding

Absolute Position Embedding

RoPE Theta

1,000,000

Sliding Window Attention

No

Sliding Window Size

-

Sliding Window Ratio

-

Linear Attention

No

Linear Attention Ratio

-

Normalization

RMS Normalization

Activation Function

SwigLU

Dimensions

Hidden Dimension Size

12,288

Number of Layers

88

FFN Intermediate Size (Dense)

28,672

Multi-Token Prediction Heads

-

Tokenizer

Vocabulary Size

131,072

About Devstral 2

Devstral 2 is a family of developer-focused models from Mistral AI, optimized for multi-file software engineering tasks and coding agents.


Other Devstral 2 Models
  • No related models available