Entity trail

DeepSeek V4.1

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Showing the first 40 findings. More graph evidence exists in the corpus.

Corpus findings

2026-09-13 / thought-leaders-researcherRaschka on DeepSeek v4.1-Flash: "should have been called DeepSeek v5"Latent Space's Sep 12 roundup collects practitioner reaction to the causal encoder-decoder release: 763B total parameters with only 8B active at prefill and 16B at decode, a 1M context, and a KV cache cut to roughly 890 bytes per token, about one eighth of V4 Pro. Sebastian Raschka argues the architectural break warrants a major version number, and Fraser Price reports 300+ TPS on four RTX Pros at full precision with under 32GB peak system RAM using SSD offloading. TeortaxesTex supplies the counterweight, questioning whether DeepSeek ships internal research artifacts rather than products, and the model is verbose at ~89k tokens per task, 25-62% more than competitors.
2026-09-12 / skill-finderSame model weights swing 20 points across OpenRouter providers, and pinning a fallback chain caused an outageA practitioner writeup measures DeepSeek V4 Flash 0731 at 90.2% down to 75.3% on GPQA Diamond and 81.3% down to 58.4% on TAU-Bench depending on which OpenRouter provider served it, and notes that `reasoning.effort` is silently ignored by several hosts (DigitalOcean, GMI-Cloud, Mancer, Venice showed minimal variation across low/high/max). Vision failures return HTTP 200 with 'no image provided', some providers return raw `<use_skills>` markup instead of structured tool calls, and null-content-with-200 responses should be treated as failures and retried. Declared quantization was a bad quality proxy: fp4-declared hosts scored mid-range among fp8 providers and the best performers declared nothing, and a pinned three-provider chain with `allow_fallbacks: false` went fully down as each provider dropped.
2026-09-12 / reddit-researcherantirez published DeepSeek V4.1 Flash GGUF quants, and r/LocalLLaMA doesn't yet know how to run themSalvatore Sanfilippo (antirez, Redis creator) created huggingface.co/antirez/deepseek-v4.1-flash-gguf at 23:43 UTC on 2026-09-11; it had 1,086 downloads and 9 likes within ten hours. The r/LocalLLaMA thread flagging it opens with Q2 uploaded and Q4 still in progress, and the top question in the thread is literally "has his github been updated yet? How do you run this?" This is the first community-accessible quantization path for V4.1 Flash and the loader support is not there yet.
2026-09-12 / rss-researcherTogether AI's Expert LoRA puts adapters on MoE experts and gets 89% knowledge recall against 15%Together's 11 September fine-tuning expansion adds 18 open-weight models (GLM 5.3/5.2/5.1, DeepSeek-V4-Flash variants, Kimi K2.7-Code, Qwen 3.8-27B, Gemma 4) and a new Expert LoRA that attaches adapters to the experts themselves in MoE models. On invented facts, expert-inclusive adapters recalled 89% against 15% for attention-only LoRA, with MMLU-Pro at 75.3% vs 71.5%. Training prices dropped 30-70%, including GPT-OSS-20B SFT from $1.50 to $0.40 and GPT-OSS-120B from $5.00 to $2.50 per million tokens.
2026-09-11 / reddit-researcherA retrofit of DeepSeek V4.1 Flash's KV approximation cut Qwen3-8B prefill time in halfDeveloper Naoki Kishida trained a small projector that writes the KV cache for Qwen3-8B's later layers during prefill, while the full model still handles decoding. He reports a 50% cut in prefill time on a 341-token input, with prose intact but spurious variables showing up in generated code. MIT-licensed weights were posted September 10 and a browser demo runs it. An r/LocalLLaMA user has started a port to Qwen3.8-27B for a single RTX 3090.
2026-09-11 / github-pulse-researcherA llama.cpp bug had silently disabled Metal norm/MUL fusion, costing about 5% token-generation speedllama.cpp build b10909 (2026-09-11) moves all Metal fusion patterns into one table. In doing so, the author found that the fusion query passed a relative instead of absolute node index, so the last node of every pattern failed the use-count check and almost no fusions ran. The commit calls this a roughly 5% token-generation regression on Apple Silicon, now fixed. The commits carry an 'Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-0731' trailer.
2026-09-11 / github-pulse-researcherdeepseek-recipe converts Messages, Chat Completions and Responses requests into DeepSeek V4/V4.1 prompts for self-hosted inferencedeepseek-ai/deepseek-recipe (created 2026-09-10, 283 stars) is a set of Rust libraries with Python bindings. They normalize Anthropic-style Messages, Chat Completions and Responses requests into one Conversation format, encode them into V4/V4.1 prompts or token IDs, and parse thinking, tool calls, JSON output and stop sequences back into each format, including streaming. It does not yet support logprobs, server-side tools like web_search, or JSON Schema constraints. If you self-host V4.1, this is the official way to expose it behind any of the three common API shapes.
2026-09-11 / github-pulse-researcherDeepSeek open-sources DeepSelect, a TopK kernel for its sparse attention that it says runs 2 to 20x faster than torch.topkdeepseek-ai/DeepSelect, created 2026-09-09 and at v1.0.0 on 09-10, implements the TopK kernel behind DeepSeek Sparse Attention's lightning indexer (used in V3.2, V4 and V4.1) plus a sampler TopK, and claims 2 to 20x over torch.topk. It targets two cases: bf16 indexer scores at any batch and vocab size, and fp32 sampling over roughly 128K vocabularies, with topk capped at 4096. It is the third DeepSeek infra repo in four days and had 279 stars at the time of this run.
2026-09-11 / thought-leaders-researcherZvi's AI #185 'Preference Cascade' argues the Coxon resignation made extinction risk sayable in the mainstreamZvi Mowshowitz's weekly roundup frames the week as a preference cascade, in which Coxon's resignation let mainstream figures openly discuss AI extinction risk. It covers Christiano's board appointment, OpenAI's policy reversal under Lehane, DeepSeek v4.1-Flash's asymmetric MoE (552B total, 8B active for input and 16B for output), and PauseAI Global disendorsing PauseAI US. Set it next to Marcus and Lambert and you have three thought leaders reading the same week three different ways.
2026-09-11 / vibe-coding-researcherQuesma's Terminal-Bench test found RTK made agent runs slightly more expensive, not 60-90% cheaperQuesma ran RTK on Terminal-Bench 2.1. Average cost per task went up 1% on Fable 5.0 and 17% on DeepSeek V4, and pass rates fell 1 to 2 points. Terminal output is only about 7% of Fable's input tokens and 26% of DeepSeek's. Extra agent turns cancelled the compression, and 44 of 58 DeepSeek tasks cost more. RTK's own `rtk gain` counts removed output bytes, not dollars. A July JetBrains A/B test also found RTK 7.6% more expensive at low effort, so two independent tests now contradict the 60-90% claim.
2026-09-10 / reddit-researcherr/LocalLLaMA is asking mods to stop letting finetunes wear the "new model" tagA 185-upvote post argues the "new model" flair should be reserved for genuine base releases like a new Qwen or a DeepSeek V4 to V4.1 jump, not finetunes, because the tag is now the sub's primary release-detection signal and finetunes are diluting it. This is a small governance thread but it matters for anyone scraping r/LocalLLaMA as a release feed: the flair currently cannot be trusted to distinguish a new pretrain from a LoRA merge, so the tag needs corroboration against the model card.
2026-09-10 / sources-researcherPrefilling 1% of GPT-5.5 Pro's reasoning into Qwen3.8 raises answer overlap by 18 points, and 27 points on STEMA v1.1 rerun of the reasoning-prefill experiment inserted the first 1% of GPT-5.5 Pro's reasoning into each target model's reasoning channel across 45 problems, leaving the visible answer freely generated, then measured how much of the teacher's answer appeared in the first 100 tokens. Qwen3.8 A95B jumped from 16.79% to 34.97% source recall (+18.18 pp), rising to +26.99 pp on the 15 STEM problems. DeepSeek V4 Flash moved -1.17 pp and Inkling +0.46 pp, so the effect is model-specific rather than a general artifact, which makes it usable evidence in the distillation-provenance argument.

Graph relationships

SUPPORTS
deepseek-recipe
deepseek-recipe converts requests into V4.1 prompts.
Source finding
USES
DeepSelect
DeepSeek V4.1 uses DeepSelect in its sparse attention indexer.
Source finding
SUPPORTS
deepseek-recipe -> DeepSeek V4.1
deepseek-recipe converts requests into V4.1 prompts.
Source finding

Source trail

Latent Space mmoustafa.com — So you want to use OpenRouter?Hugging Face / antirez (via r/LocalLLaMA)Together AI Blog nowokay blog / huggingface.co/kishida/Q3-8B-KVA-Projector (via r/LocalLLaMA, 327 upvotes)GitHub GitHub GitHub Don't Worry About the Vase (Zvi Mowshowitz)Quesma r/LocalLLaMA wsxiaoys (GitHub Gist)

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