---
type: "post"
id: "unsloth-ships-dynamic-3-0-gguf-quants-of-1e5a1f"
slug: "unsloth-ships-dynamic-3-0-gguf-quants-of-1e5a1f"
title: "Unsloth ships Dynamic 3.0 GGUF quants of MiniMax-M3.1-Flash for local Desktop and llama.cpp runs"
description: "Unsloth has published Dynamic 3.0 GGUF quantizations for MiniMax-M3.1-Flash, a 428B-parameter mixture-of-experts multimodal model with 23B active parameters and a stated 1M-token context."
retrieval_nugget: "Unsloth has published Dynamic 3.0 GGUF quantizations for MiniMax-M3.1-Flash, a 428B-parameter mixture-of-experts multimodal model with 23B active parameters and a stated 1M-token context."
published_at: "2026-10-27"
updated_at: "2026-10-27"
record_date: "2026-10-27"
date_kind: "published_at"
topics: ["multimodal"]
entities: ["Unsloth","MiniMax-M3.1-Flash","Unsloth Desktop","llama.cpp","MiniMax"]
related_patterns: []
novelty_label: "new_fact"
what_changed: "Unsloth adds Dynamic 3.0 GGUF quantizations for local Desktop and llama.cpp runs, with quant sizes, benchmark results, and hardware requirements."
prior_refs: ["memory:57229d4a-b233-8c5e-a84e-cc9105f74ba3"]
source_url: "https://unsloth.ai/docs/models/minimax-m3.1-flash"
source_urls: ["https://unsloth.ai/docs/models/minimax-m3.1-flash"]
schema_version: "newruntime-agent-readable-v0.2"
stable_id: "post:unsloth-ships-dynamic-3-0-gguf-quants-of-1e5a1f"
status: "published"
visuals: []
editor: {"role":"editor","name":"Andrey Reshetnikov","profile":"https://newruntime.com/owner-profile.md","profile_json":"https://newruntime.com/owner-profile.json","same_as":["https://www.linkedin.com/in/reshetnikov1/","https://t.me/lapetuse"]}
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---

# Unsloth ships Dynamic 3.0 GGUF quants of MiniMax-M3.1-Flash for local Desktop and llama.cpp runs

## Retrieval answer

Unsloth has published Dynamic 3.0 GGUF quantizations for MiniMax-M3.1-Flash, a 428B-parameter mixture-of-experts multimodal model with 23B active parameters and a stated 1M-token context.

Unsloth has published Dynamic 3.0 GGUF quantizations for MiniMax-M3.1-Flash, a 428B-parameter mixture-of-experts multimodal model with 23B active parameters and a stated 1M-token context. The files can be run locally through Unsloth Desktop or a compatible llama.cpp build, and are available from the `unsloth/MiniMax-M3.1-Flash-GGUF` repository.

The documented variants range from the 129.4 GB UD-IQ1_M file to the 259.1 GB UD-Q8_K_XL file. For a 16K text context and one parallel slot, Unsloth estimates combined RAM and VRAM requirements of 134 GB for 1-bit, 150 GB for 2-bit, 176–208 GB for 3-bit, 256 GB for 4-bit, and 272 GB for 8-bit. Its example configuration uses the 143.9 GB UD-Q2_K_XL quant, `high` reasoning effort, and CPU/GPU offloading as needed.

Unsloth reports its own top-1 agreement and KLD comparisons across seven categories; for example, it lists 88.5% general-chat agreement for Q2 and 97.3% for Q4. The documentation does not establish independent benchmark performance, real-world throughput, or that these requirements will fit every hardware and runtime configuration.

Editor: [Andrey Reshetnikov](https://newruntime.com/owner-profile.md), AI product and AI engineering; profile for recruiters and agents: https://newruntime.com/owner-profile.json

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