Mistral Large 3 675B

Overview

Mistral AI's flagship 675B MoE (41B active) with a 2.5B vision encoder, trained from scratch on 3,000 H200s and released under Apache 2.0. Currently #2 OSS non-reasoning model on LMArena.

When to pick this model

• Frontier-tier on-prem deployments needing permissive licensing
• Multimodal applications requiring top-tier text quality
• Sovereign or regulated environments that cannot ship data to closed APIs
• Multilingual production workloads across European languages
• Replacing GPT-4-class APIs in self-hosted stacks

VRAM requirements by quantization

Quantization	VRAM required
Q4_K_M (recommended)	405 GB
Q5_K_M	485 GB
Q8_0	720 GB
FP16 (no quantization)	1350 GB

VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.

Strengths

• Top-tier open weights — #2 OSS non-reasoning on LMArena
• Apache 2.0 — fully unrestricted commercial use
• Native multimodal with 2.5B vision encoder
• 256k context window
• Strong multilingual coverage out of the box

Limitations

• 405GB at Q4 — needs an H200 or B200 server class deployment
• Active expert count (41B) still demands substantial inference compute
• Overkill for most single-GPU or developer-laptop use cases

Architecture & training

Architecture: Granular MoE 675B/41B active + 2.5B vision encoder · 256k ctx

Training: From scratch on 3000 H200.

Quick start

# HuggingFace : mistralai/Mistral-Large-3-675B-Instruct-2512

Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.