---
type: "post"
id: "ist-austria-daslab-releases-gsq-rco-non-uniform-f02d77"
slug: "ist-austria-daslab-releases-gsq-rco-non-uniform-f02d77"
title: "IST Austria DASLab releases GSQ-RCO non-uniform GGUF quantizations of DeepSeek-V4.1-Flash-1017 at 1.75-2.25 bits per weight"
description: "ISTA DASLab at the Institute of Science and Technology Austria has published three GSQ-RCO GGUF quantizations of DeepSeek-V4.1-Flash-1017 on Hugging Face."
retrieval_nugget: "ISTA DASLab at the Institute of Science and Technology Austria has published three GSQ-RCO GGUF quantizations of DeepSeek-V4.1-Flash-1017 on Hugging Face."
published_at: "2026-10-24"
updated_at: "2026-10-24"
record_date: "2026-10-24"
date_kind: "published_at"
topics: ["local-models","open-models","inference"]
entities: ["ISTA DASLab","Institute of Science and Technology Austria","DeepSeek-V4.1-Flash-1017","GSQ-RCO","Strata","llama.cpp"]
related_patterns: []
novelty_label: "new_fact"
what_changed: "ISTA-DASLab now provides GSQ-RCO GGUF quantizations for DeepSeek-V4.1-Flash-1017 at 1.75, 2.00, and 2.25 bits per weight, rather than the previously covered Qwen3.8-Flash-Next build."
prior_refs: ["memory:9fdd37e6-8830-8884-a0d2-f191ae3a4744","memory:09cb5ade-a0c0-82fd-af91-d10fb1d4d731","memory:ff32b7e1-5b16-89c5-a4e0-5b62412c4cfa"]
source_url: "https://huggingface.co/ISTA-DASLab/DeepSeek-V4.1-Flash-1017-GSQ-RCO-GGUF"
source_urls: ["https://huggingface.co/ISTA-DASLab/DeepSeek-V4.1-Flash-1017-GSQ-RCO-GGUF"]
schema_version: "newruntime-agent-readable-v0.2"
stable_id: "post:ist-austria-daslab-releases-gsq-rco-non-uniform-f02d77"
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/ist-austria-daslab-releases-gsq-rco-non-uniform-f02d77/","markdown":"https://newruntime.com/posts/ist-austria-daslab-releases-gsq-rco-non-uniform-f02d77.md","json":"https://newruntime.com/posts/ist-austria-daslab-releases-gsq-rco-non-uniform-f02d77.json"}
---

# IST Austria DASLab releases GSQ-RCO non-uniform GGUF quantizations of DeepSeek-V4.1-Flash-1017 at 1.75-2.25 bits per weight

## Retrieval answer

ISTA DASLab at the Institute of Science and Technology Austria has published three GSQ-RCO GGUF quantizations of DeepSeek-V4.1-Flash-1017 on Hugging Face.

ISTA DASLab at the Institute of Science and Technology Austria has published three GSQ-RCO GGUF quantizations of DeepSeek-V4.1-Flash-1017 on Hugging Face. The source describes the base as a 763.2B-parameter, 384-expert mixture-of-experts model, with six routed experts active per token and a 1M-token context window.

The releases are IQ1_M at 1.75 bits per weight and 231.32 GB total, IQ2_XXS at 2.00 bits and 248.84 GB, and IQ2_XS at 2.25 bits and 265.83 GB. A shared 110.60 GB Engram lookup-table file is required alongside four backbone shards. The repository recommends IQ2_XXS; its reported task average is 93.54, matching the cited FP8/FP4 base on AIME25, GPQA-Diamond, and LiveCodeBench v6 at reasoning effort 50.

The card says the files run with Strata on one NVIDIA GPU with 12 GB of VRAM by using system memory and memory-mapping the Engram tables from SSD. It also says llama.cpp did not support DeepSeek-V4.1 as of October 23, 2026. The source does not establish independent benchmark replication, real-world throughput, total host-memory requirements, or performance on hardware other than the stated setup.

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

## Continue in the graph

Next records for an agent, linked to their Markdown twins. Replace `.md` with `.json` for structured fields, including `next_reads`.

- topic hub: [Local Models](https://newruntime.com/topics/local-models.md): Explore the local-models topic hub.
- topic hub: [Open Models](https://newruntime.com/topics/open-models.md): Explore the open-models topic hub.
- topic hub: [Inference](https://newruntime.com/topics/inference.md): Explore the inference topic hub.
- collection: [Field Notes](https://newruntime.com/posts.json): Open the latest editorial analysis.
- collection: [Source Ledger](https://newruntime.com/source-ledger.json): Inspect the public source evidence graph.
- whole graph: [discovery graph](https://newruntime.com/discovery-graph.json) (typed nodes and edges) and [site index](https://newruntime.com/site-index.md)