What are the downsides of DeepSeek-V4.1-Flash-1017 GSQ-RCO Coder (GGUF)?

KL 0.777 on multilingual text against 0.138 for the unpruned set; still 182.72 GB on disk; the Engram table needs NVMe; calibration datasets not published; broken tool calls when streaming from SSD in 1 of 5 Strata runs.

How much does DeepSeek-V4.1-Flash-1017 GSQ-RCO Coder (GGUF) cost for a team or in production?

No hosted API for this build; the cost is hardware. Strata's figures run from about 100 tokens a second on an RTX PRO 6000 Blackwell (test build) to 59.2 on four R9700s and 20.9 on one R9700 with RAM capped at 40 GB, falling to 8.0 at 16 GB. Budget 182.72 GB of NVMe space for llama.cpp or 191 GB for Strata.

Is DeepSeek-V4.1-Flash-1017 GSQ-RCO Coder (GGUF) safe for company data?

MIT licence with no use restrictions, inherited from the base model. Everything runs locally; the calibration datasets are not published.

What are the best DeepSeek-V4.1-Flash-1017 GSQ-RCO Coder (GGUF) alternatives?

The closest alternatives we have reviewed are DeepSeek-V4.1-Flash, Qwen3.8-Flash-Next, Qwen3.8-27B and DeepSeek-V4-Pro.

DeepSeek-V4.1-Flash-1017 GSQ-RCO Coder (GGUF) review (2026)

What is DeepSeek-V4.1-Flash-1017 GSQ-RCO Coder (GGUF)?

This is a compressed build of DeepSeek-V4.1-Flash-1017, published on Hugging Face on 2 November 2026 by ISTA-DASLab, the Distributed Algorithms and Systems Lab at the Institute of Science and Technology Austria. It deletes 192 of the 384 routed experts in every layer and quantises the rest, so the 551.9B-parameter transformer is stored in 72.12 GB, against 138.2 GB for the lab's unpruned IQ2_XXS build. The lab calls it 1 bit per parameter: that is 1.05 bpw averaged over the original transformer with deleted experts counted as zero, while the 280.1B parameters that remain average 2.06 bpw.

The name comes from two of the lab's methods. GSQ is post-training scalar quantisation that learns grid assignments and group scales together through Gumbel-Softmax sampling; the lab says it closes most of the gap to vector quantisation at low bit-widths while still writing standard GGUF types. RCO compresses to an exact budget by optimising on a Riemannian manifold; here it chose which experts to drop by minimising the KL divergence between the pruned and unpruned models on calibration data aimed at code, agentic tool use, long context and vision. The calibration datasets are not published.

The weights keep the base model's MIT licence. The repository holds five IQ2_XXS shards (the first is metadata only), the 110.60 GB Engram lookup file at IQ4_NL (4.50 bpw), which is neither pruned nor searched and is linked in as shard six, and a 0.97 GB vision projector: 182.72 GB in all, against 248.84 GB for the unpruned build. It runs in llama.cpp and in Strata, and Hugging Face counted 127,948 downloads by 5 November 2026.

How much does DeepSeek-V4.1-Flash-1017 GSQ-RCO Coder (GGUF) cost?

Plan	Price	What you get
Weights	Free	MIT licence, inherited from DeepSeek-V4.1-Flash-1017
DeepSeek API (base model)	$0.15 in / $0.60 out per 1M tokens off-peak	Full base model on DeepSeek's servers, not this build

Free MIT-licensed weights, 182.72 GB of GGUF files including the Engram table; the cost is the hardware. The full base model on DeepSeek's API costs $0.15 in / $0.60 out per 1M tokens off-peak.

Is DeepSeek-V4.1-Flash-1017 GSQ-RCO Coder (GGUF) worth it?

This build puts most of DeepSeek-V4.1-Flash's coding ability on a single workstation, and what it gives up is nearly everything that is not code. On ISTA-DASLab's own runs at reasoning effort 50 it scores 77.00 on SWE-bench Verified against 81.20 for the base FP8/FP4 model (94.8% retained) and 88.57 against 89.71 on LiveCodeBench v6 (98.7%). The lab puts the larger SWE-bench drop down to errors compounding over long multi-turn agent tasks. We found no independent SWE-bench run.

Strata's measurements show where the loss lands. Comparing output distributions with the full model over 4,995 positions, the Coder has a KL divergence of 0.397 and picks the same top token 76.4% of the time, against 0.171 and 83.7% for the unpruned 2.00 bpw set; perplexity rises from 3.140 for the full model to 4.645. Split by content, KL is 0.230 on code and 0.199 on agent traces, but 0.476 on English prose and 0.777 on multilingual text, where the unpruned set holds 0.138. Strata's own 61-task suite passes 54. The model card is candid about this: it calls the release experimental and points general use to the unpruned GSQ-RCO builds.

The one-GPU claim needs careful reading. The 72.1 GB transformer is what goes on the GPU; the 110.6 GB Engram table stays memory-mapped on disk and is read a row per token, so a fast NVMe drive is part of the specification, and the card gives no RAM or VRAM figures. Strata's 0.2.10 release notes fill the gap: 20.9 to 21.9 tokens a second on a 128 GB Ryzen AI Max, 19.1 to 22.5 on an Apple M5 Max with 74 GB peak memory, and 59.2 on four Radeon AI PRO R9700 cards. A user writing on note.com measured an average of 29.8 tokens a second on two RTX 3090s with 64 GB of RAM at 64K context, about three times the 10.1 of an unpruned UD-IQ1_M build on the same machine.

Against the base model the trade is in fast memory, not disk. Our review of DeepSeek-V4.1-Flash puts the official checkpoint at about 765 GB, beyond an eight-way H100 node; the lab's unpruned IQ2_XXS is 248.84 GB and this one 182.72 GB, with the shared Engram file making up 110.60 GB of both. Pruning shrinks only the part that has to sit in VRAM or RAM, which is what sets the speed on consumer machines. For teams that can send code to DeepSeek's API, $0.15 per million input tokens off-peak costs less than any of this hardware; the Coder is for code that must stay on your own machines.

The rough edges are documented. Strata lists Windows with an NVIDIA card as not yet run, answers can differ between starts even at temperature 0, and when most of the model streams from the SSD one run in five produced a broken tool call with a DSML marker inside the argument. With the model fully in memory, all 8 tool calls came back correct.

Where it earns its place

A developer with two RTX 3090s and 64 GB of RAM
Run a local coding agent on a private repository
Run strata launch --model ISTA-DAS-Coder in Strata 0.2.10, a 191 GB first download including an 8 GB drafter, and point Claude Code or OpenCode at the localhost API. One user measured an average of 29.8 tokens a second at 64K context; a Strata pull request measured 25.27 at 8K on two 3090s with 121 GB of RAM.
The CTO of a 20-person software consultancy bound by client confidentiality
Get DeepSeek-class coding help without DeepSeek's China-hosted API
One 128 GB Ryzen AI Max machine runs it at 20.9 to 21.9 tokens a second according to Strata's release notes, with no per-token bill and MIT terms. Keep client documentation in other languages on the unpruned build, whose multilingual KL is 0.138 against the Coder's 0.777.
An ML researcher studying model compression
Compare expert pruning with plain low-bit quantisation on one model
The repository's kept-experts.json lists the 192 experts kept per layer, and the lab's unpruned GSQ-RCO repository offers IQ1_M (1.75 bpw), IQ2_XXS (2.00) and IQ2_XS (2.25) of the same checkpoint. Strata's per-domain KL table gives a ready baseline for code, agent, document, English and multilingual text.

Before you commit

Setup: Download with hf download --include "IQ2_XXS/*" "*Engram*", link the Engram file in as shard 6 (ln -s, or mklink /H on Windows), and run llama.cpp b11870 or newer with -m on shard 1 and -ngl 99, adding --mmproj for images and --reasoning on --reasoning-effort 50. In Strata 0.2.10 one command downloads 191 GB, or about 80 GB if the Engram file is already present.
Integrations: llama.cpp (llama-cli, llama-server, and llama-mtmd-cli with --jinja for vision) and Strata, which serves OpenAI- and Anthropic-compatible APIs on localhost. The lab says the builds were tuned together with Strata.
Data and privacy: MIT licence with no use restrictions, inherited from the base model. Everything runs locally; the calibration datasets are not published.
Cost at scale: No hosted API for this build; the cost is hardware. Strata's figures run from about 100 tokens a second on an RTX PRO 6000 Blackwell (test build) to 59.2 on four R9700s and 20.9 on one R9700 with RAM capped at 40 GB, falling to 8.0 at 16 GB. Budget 182.72 GB of NVMe space for llama.cpp or 191 GB for Strata.
Limits: Strata defaults to 8,192 tokens of context, accepts up to 1M and has tested nothing past 256K. Hot-expert profiles built for 384 experts will not load. Windows with NVIDIA is untested in Strata 0.2.10, output varies between starts at temperature 0, and quality drops most on English prose and multilingual text.

What’s good

77.00 on SWE-bench Verified, 94.8% of the base model (self-reported)
72.1 GB transformer against 138.2 GB unpruned
MIT licence; image input kept through the 0.97 GB projector
About 30 tokens a second on two RTX 3090s with 64 GB RAM (user report)

What’s not

KL 0.777 on multilingual text against 0.138 for the unpruned set
Still 182.72 GB on disk; the Engram table needs NVMe
Calibration datasets not published
Broken tool calls when streaming from SSD in 1 of 5 Strata runs

Best for

Developers with 64 GB or more of RAM, a fast NVMe drive and one or two large GPUs who want DeepSeek-V4.1-Flash for coding agents without sending code to an API.

DeepSeek-V4.1-Flash-1017 GSQ-RCO Coder (GGUF) alternatives