---
type: "post"
id: "unsloth-ships-dynamic-v3-1-gguf-quants-of-b32b5f"
slug: "unsloth-ships-dynamic-v3-1-gguf-quants-of-b32b5f"
title: "Unsloth ships Dynamic v3.1 GGUF quants of DeepSeek-V4.1-Flash-1017 and disputes GSQ-RCO's benchmark claims"
description: "Unsloth has released preview Dynamic v3.1 GGUF quantizations for DeepSeek-V4.1-Flash-1017 and published its own cross-benchmark comparison with GSQ-RCO alternatives."
retrieval_nugget: "Unsloth has released preview Dynamic v3.1 GGUF quantizations for DeepSeek-V4.1-Flash-1017 and published its own cross-benchmark comparison with GSQ-RCO alternatives. The practical signal for teams running large local models is not that one chart settles the contest, but that quant choice affects accuracy, reasoning length, truncation, storage, and the reproducibility of reported results."
published_at: "2026-10-30"
updated_at: "2026-10-30"
record_date: "2026-10-30"
date_kind: "published_at"
topics: ["coding-agents","evals"]
entities: ["Unsloth","DeepSeek-V4.1-Flash-1017","Unsloth Dynamic v3.1","GSQ-RCO","ISTA-DASLab","llama.cpp"]
related_patterns: ["verification-bandwidth-is-the-scarce-resource"]
novelty_label: "new_fact"
what_changed: "Unsloth now offers Dynamic v3.1 GGUF quants for the same DeepSeek model, and reports benchmark results disputing GSQ-RCO’s AIME25 recovery claims."
prior_refs: ["memory:5131f673-01bf-8cc9-a829-bc7632819f00","memory:8bfd6b63-c5fc-839b-a294-41f2dbf7a7a4"]
source_url: "https://unsloth.ai/docs/models/deepseek-v4.1-flash"
source_urls: ["https://unsloth.ai/docs/models/deepseek-v4.1-flash"]
schema_version: "newruntime-agent-readable-v0.2"
stable_id: "post:unsloth-ships-dynamic-v3-1-gguf-quants-of-b32b5f"
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"]}
routes: {"html":"https://newruntime.com/posts/unsloth-ships-dynamic-v3-1-gguf-quants-of-b32b5f/","markdown":"https://newruntime.com/posts/unsloth-ships-dynamic-v3-1-gguf-quants-of-b32b5f.md","json":"https://newruntime.com/posts/unsloth-ships-dynamic-v3-1-gguf-quants-of-b32b5f.json"}
---

# Unsloth ships Dynamic v3.1 GGUF quants of DeepSeek-V4.1-Flash-1017 and disputes GSQ-RCO's benchmark claims

## Retrieval answer

Unsloth has released preview Dynamic v3.1 GGUF quantizations for DeepSeek-V4.1-Flash-1017 and published its own cross-benchmark comparison with GSQ-RCO alternatives. The practical signal for teams running large local models is not that one chart settles the contest, but that quant choice affects accuracy, reasoning length, truncation, storage, and the reproducibility of reported results.

## Summary

Unsloth has released preview Dynamic v3.1 GGUF quantizations for DeepSeek-V4.1-Flash-1017 and published its own cross-benchmark comparison with GSQ-RCO alternatives. The practical signal for teams running large local models is not that one chart settles the contest, but that quant choice affects accuracy, reasoning length, truncation, storage, and the reproducibility of reported results.

## What happened

Unsloth updated its local-run guide for DeepSeek-V4.1-Flash-1017 on October 30, 2026 with GGUF files made using its preview Dynamic v3.1 algorithm. GGUF is a model-file format used by local inference tooling including llama.cpp; quantization reduces a model’s storage and memory footprint by representing weights with fewer bits. The model in question is large: Unsloth describes a 552B-parameter backbone plus 196B Engram parameters, with a one-million-token context window. Its guide says SSD offloading can reduce RAM or VRAM requirements, and that local operation is possible on systems with 128 GB or more RAM, or 64 GB RAM more slowly.

The company provides several new file sizes. Its lossless UD-Q8_K_XL file is listed at 508.4 GB and its near-lossless UD-Q4_K_XL at 500.7 GB. Lower-bit examples include UD-Q2_K_XL at 334.8 GB, UD-IQ2_XXS at 300.5 GB, UD-IQ1_M at 250.4 GB, and UD-IQ1_S at 233.0 GB. Unsloth says Unsloth Desktop plus llama.cpp can deliver roughly three times faster inference than other CPU/VRAM/ngram repositories, but this is its own performance claim rather than an independent measurement.

The update is also a public disagreement over benchmarking. Unsloth says it tested its older Dynamic v3 and new v3.1 quants against GSQ-RCO variants on AIME25, Aider Polyglot, LiveCodeBench v6, HMMT 2026, GPQA Diamond, MMLU-Pro, and IFEval. It reports a six-benchmark average excluding Aider Polyglot, saying the exclusion is conservative because including Aider would make its reported gap larger. In the published table summarized with the release, a UD-Q8_K_XL reference averages 87.68, UD-Q2_K_XL averages 87.49, UD-IQ1_S averages 83.53, and GSQ-RCO IQ1_M averages 81.78.

Unsloth specifically contests GSQ-RCO’s reported 100% AIME25 result across all three of its quants. It says that, using 30 problems and 16 samples per problem—480 answers per quant—with temperature 1.0 and reasoning effort 50, GSQ-RCO retained 94.64% of baseline rather than 100%; the supplied release summary gives 91.88% for the one-bit quant. Unsloth also argues that KL divergence and top-1% agreement on held-out tests remain useful quality measures. Its additional operational claim is that lower-bit quants tend to generate longer reasoning traces and therefore encounter more context truncation; it says its quants truncated less often than GSQ’s in its six-benchmark analysis.

## What the archive adds

This is a new fact in an active local-quantization story, rather than a restatement. [ISTA-DASLab’s GSQ-RCO release](https://newruntime.com/posts/ist-austria-daslab-releases-gsq-rco-non-uniform-f02d77/) established the competing DeepSeek-V4.1-Flash quant family at 1.75, 2.00, and 2.25 bits per weight. [Unsloth’s Dynamic 3.0 MiniMax release](https://newruntime.com/posts/unsloth-ships-dynamic-3-0-gguf-quants-of-1e5a1f/) provided the immediate precedent: local GGUF releases increasingly arrive with size, hardware, and benchmark claims together.

## Trend signal

No supplied archive assumption is genuinely specific enough to establish a broader trend beyond this quantization release and its contested measurements.

## Why now

The timing follows the availability of DeepSeek-V4.1-Flash-1017 and a competing GSQ-RCO quant release. Unsloth’s October 30 Dynamic v3.1 update turns that comparison into a direct claim that earlier accuracy reporting could not be reproduced under its stated evaluation setup. The release therefore functions as both a new artifact drop and a challenge to a recent measurement narrative.

## Connections to other events

The earlier GSQ-RCO note concerned the same DeepSeek-V4.1-Flash-1017 model and its low-bit GGUF variants, so the two releases are directly comparable at the level of local model packaging. Separately, the earlier Unsloth Dynamic 3.0 note showed the company using the same broad strategy—shipping local GGUFs with hardware and benchmark guidance—for MiniMax-M3.1-Flash. This release extends that approach to a substantially larger DeepSeek model.

## Why it matters

For a team considering local deployment, the useful comparison is not simply “smallest quant” or “highest published score.” Storage requirements here span roughly 233 GB to 508 GB, while the source also flags a quality trade-off and a behavior trade-off: lower-bit variants may reason for longer and be truncated more often. That can alter task completion even where an aggregate benchmark appears acceptable.

The competing claims also make procurement and evaluation work unavoidable. A benchmark score from the quantizer is evidence, not a deployment guarantee—especially when another quantizer’s published result is being disputed. Teams choosing a local model for coding or agent workloads should validate on their own prompts, tool-use loops, context budgets, and hardware rather than treating a single AIME25 claim as a universal ranking.

## What happens next

- **More local-quant releases will publish multi-benchmark comparisons rather than a single headline score.** (3 months, 65%). Watch for: release notes that disclose evaluation suites, sampling settings, and aggregate methodology.
- **Adopters will test truncation and long-reasoning behavior alongside accuracy.** (6 months, 60%). Watch for: benchmark tables reporting completion rates or context-limit failures.

## Limits and uncertainty

The evidence is a release page from Unsloth, which is both the producer of Dynamic v3.1 and the party challenging GSQ-RCO. It reports a methodology and results, but the supplied material does not provide an independent replication, full raw outputs, a neutral benchmark operator, or a complete cost and throughput comparison across identical hardware. “Best in class,” faster-inference, and broad quality claims remain vendor assertions. The material also does not establish which quant will perform best on a particular codebase, language, tool environment, or agent harness.

## Skill to build now

Create a small quant-selection evaluation before downloading hundreds of gigabytes. Choose a representative set of coding or agent tasks, fix sampling settings and context budgets, and record four outputs for each candidate: task completion, truncation rate, latency on the intended machine, and disk/RAM use. Run enough repeated samples to separate a one-off success from a stable result. That turns a vendor benchmark into an input to a decision rather than the decision itself.

## Reader takeaway

Dynamic v3.1 gives local DeepSeek-V4.1-Flash users new size-versus-quality options, but its larger lesson is methodological. When quantizers disagree, benchmark scores alone are not a safe buying signal. Evaluate the exact quant on the workload and context constraints that matter to your team.

## Open question

Will an independent evaluator reproduce either side’s AIME25 results under a shared, fully specified harness and publish the raw completion and truncation data?

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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