AI
Introducing DeepSeek-V4.1-Flash-1017 Dynamic v3.1 Unsloth GGUFs
Publication: top scoring links : multi
Author: /u/danielhanchen
Source published: 30/10/2026, 13:48:01 UTC
Briefing created: 31/10/2026, 00:56:22 UTC
Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 30/10/2026, 17:47:25 UTC.
Briefings
DeepSeek-V4.1-Flash-1017 Dynamic v3.1 GGUFs released in October 2026 demonstrate superior accuracy across seven benchmarks compared to competing models like GSQ-RCO, with AIME25 showing ~91.88% recovery versus claimed 100%. The model supports 128GB unified memory and 64GB with engram offloading, and KLD scores indicate efficient quantization without perfect model equivalence.
Key points
- DeepSeek-V4.1-Flash-1017 Dynamic v3.1 outperforms competing models on nearly all benchmarks, with AIME25 showing 91.88% accuracy recovery versus claimed 100% by GSQ-RCO.
- The model supports running on 128GB unified memory or 64GB with engram offloading, and KLD scores confirm efficient quantization without perfect model equivalence.
Context
Released October 2026, DeepSeek-V4.1-Flash-1017 Dynamic v3.1 GGUFs showcase Unsloth's accuracy retention across 7 benchmarks, outperforming GSQ-RCO on most metrics including AIME25, LiveCodeBench, and GPQA Diamond.
Limitations
Benchmark data includes 16 runs for AIME25 with averaged results; some reported claims like 100% accuracy recovery were not reproduced in testing; KLD analysis shows quantized models differ from 16bit originals.
Generated by Bittrees-hosted qwen35-2b sha256:aaf42c8b7c3c on 31/10/2026, 00:56:22 UTC. Based on publisher-provided feed evidence; may not cover the full original article. Check the source for details.
View archived briefing on IPFS
bafybeiba5w5xhzxyuz5aa6mujqkx23xkuyihx35gdm4sr4qys67s2xguvq