Xinyu Yang retweeted
We're sorry for the rate limits and unnecessary quota consumption on the K3.1 Pro subscription plan. These were our issues, and we should have fixed them sooner.
Today, every subscriber on every plan gets a one-time usage reset, restoring 25% of their monthly quota (usage below 25% resets to zero), equal to one week of usage. Your usage bar will reflect this adjustment by end of day PST.
We've also adjusted the plan's parameters and are reducing unnecessary subagent calls and output length, so your quota goes further.
Thank you for your patience while we put this right.
卧槽,3090要可能要涨价了!!
因为老黄的刀法被人破解了
有人给3090解了个锁,同样限制在 360W,PyTorch 测试从 75 TFLOPS 跑到了 135 TFLOPS,提升约 80%!
你以为是超频,把功耗往上拉?
但他改的是显卡里控制运算速度的寄存器,这些寄存器平时有权限保护,正常改不了
他利用 GPU 启动固件的漏洞,赶在 NVIDIA 驱动加载之前拿到写入权限,把相关运算改成全速,再交给原版驱动使用
解开的主要是 FP16/BF16 输入、FP32 累加这类 AI 运算的速度限制 但测试算力多了 80%!!
4090 也有分支项目做到了:同类运算的指令吞吐,从约 165 TFLOPS 提升到 331 TFLOPS,接近翻倍
项目地址,附上👇
github.com/Zagitta/gpu-unloc…
Xinyu Yang retweeted
Hi all, I have a new result I'm excited to share. It turns out that *all existing implementations* of softmax and cross-entropy (that's JAX, PyTorch, textbooks, etc) have a subtle bug --- two bugs, actually. A simple rewrite fixes both, and eliminates "slingshots" when grokking:
Xinyu Yang retweeted
DeepInfer set the bar. SGLang pushed it higher! 🚀
SGLang now matches DeepInfer performance on BOTH prefill and decode:
• Prefill: Up to 1.04× throughput on 4× GB300 and 4× B300
• Decode: Up to 7.4% throughput on 64× GB300
We also provide an end-to-end reproduction guide and a full serving stack to run it in production.
Xinyu Yang retweeted
I asked #KimiK31 to deconstruct a 911, and turn every component into cinema.
The result: 30 seconds of AI-generated visuals, precision motion graphics, and a pulse-built soundtrack.
Concept film, not affiliated with Porsche AG
Xinyu Yang retweeted
I have to ask people not to spread this slop
@scut_longhui explains their policy pretty candidly
I disagree with it (If datasets are conflicting and one has strictly richer signal for your purposes, discard the other and clearly state the provenance), but it is what it is.
🤯炸了!梁文峰的实力被Kimi用“图表技巧”藏起来了!同一组标准Kimi竟然偷换数据来凸显自己!
1️⃣Kimi官方把DeepSeek V4.1 Flash 1017画成几乎看不见的小红点,自己则高高在上。表面上比分数Kimi 65.7% vs DS 42.3%,真正被弱化的是成本,Kimi K3.1 Pro单任务成本大约是DeepSeek的20倍。
2️⃣更关键的是Kimi官方将DeepSeek的TB4.0数据给偷换了,来源不一致,表格用DeepSeek自己公布的42.3,图表却单独换了Artificial Analysis的数据。
DeepSeek开源、极便宜、迭代快;
有人选择用“小点”把它藏起来。
分数好看,成本效率才是落地关键。
#DeepSeek #Kimi #TerminalBench #梁文峰 #开源AI #成本效率 #V41Flash1017 #K31Pro
Replying to @uniartisan
Happy one Kimi year anniversary!
Xinyu Yang retweeted
Kimi K3.1 Pro is very good at 3D modeling.
I asked Kimi K3.1 to create a 3D website that pulls apart a robotic hand into 2,317 pieces. It did pretty well!
You can just build things.
Project link: form-01-score.ok.kimi.link/
Xinyu Yang retweeted
to prepare for an upcoming release (👀) we dug into the pretokenizer a little more. are people aware that hand-crafted regexes are part of every frontier LLM and doing some pretty heavy lifting?
some quick examples 🧵
Xinyu Yang retweeted
Kimi K3.1 Pro launches at 4th on the Artificial Analysis Intelligence Index scoring 55, making large gains in agentic performance; it will be the leading open-weights model once its weights are published
@Kimi_Moonshot's new reasoning model gains 11 points over Kimi K3 (max, 44). It trails only Claude Opus 5.5 (max with fallback, 58), Claude Sonnet 5.5 (max with fallback, 56), and Muse Wonder Full (max, 56) on the Intelligence Index.
Once the weights for K3.1 Pro are released it will be the highest-scoring open weights model and the gap between the leading open weights and proprietary models on the Intelligence Index will narrow from 12 points to 3.
Key results:
➤ Large gains in agentic performance: Kimi K3.1 Pro scores 3rd on GDPval-AA with 1,846 Elo, up 312 Elo from Kimi K3, behind only Claude Opus 5.5 (1,866 Elo) and Muse Wonder Full (1,857 Elo). Kimi K3.1 Pro also achieves the highest score of any model on Terminal-Bench 4.0. K3.1 Pro scores 67%, up from 13% for Kimi K3 and ahead of Claude Sonnet 5.5 (64%). AutomationBench-AA, which tests agentic SaaS workflows, rises from 58% to 73%.
➤ Gains in other evals are more moderate: Humanity's Last Exam rises from 47% to 52%, CritPt from 23% to 25%, and SciCode from 59% to 61%. AA-Omniscience Index rises from 20 to 30, as the hallucination rate falls from 53% to 44% and accuracy rises from 48% to 51%.
➤ Cost per Task roughly equal to Kimi K3 at $1.98 per Task: K3.1 Pro uses 55K output tokens per Intelligence Index task, 14% more than Kimi K3 at 48K, while its first-party token prices are 33% lower.
Additional model details:
➤ Context window: 1M tokens.
➤ Size: 2.8T total, 104B active parameters
➤ Input modalities: Text and image
➤ Weights and license: Weights currently not published, Moonshot is expected to release the weights soon after launch
➤ Pricing: $2/$10/$0.20 per 1M input/output/cache hit tokens, 33% lower than Kimi K3 on all three rates.
Congratulations to the team at @Kimi_Moonshot on the release!
Come give it a try 👀
Introducing Kimi K3.1 Pro: our most capable 3T-class open-weight model.
🔹 1M-token context, 2.8T total / 104B active params (MoE), Multimodal.
🔹 Full-project coding: games in Unreal, Unity & Godot.
🔹 Excels at parametric 3D modeling in Blender & CAD.
🔹 More reliable at finance work: Accurate modeling with hallucination down by 65%.
🔹 Matching the strongest proprietary models across coding, agentic and knowledge benchmarks.
Kimi K3.1 Pro and K3.1 Pro Highspeed (2–3× faster) are now live on Kimi.ai, Kimi Work, Kimi Code, and the Kimi API.
Open weights by Nov 4, 2026.
🔗 API: platform.kimi.ai
🔗 Tech blog: kimi.ai/blog/kimi-k3-1-pro
Xinyu Yang retweeted
1/9 Current optimizers are static during training. What if the optimizer could think and react while the network trains?
We let an agent watch the training dynamics and rewrite the optimizer's source code as training goes: a reactive optimizer.
In every language and image setting we tried that has no tuned recipe, it matches or beats the best recipe one can one-shot, and it guards against loss spikes and overfitting by itself. In the near future, optimization decisions may belong to agents, not humans.
📝 xingjianbai.com/reactive-opt…
💻 Try it on your task: github.com/xingjian-bai/opal
🧵
Xinyu Yang retweeted
We’ve developed the world’s most complete megakernel framework. It can perform entire training runs, serve inference, play video games, and even do all three simultaneously in a single kernel.
Xinyu Yang retweeted
Introducing Flint, @Recursive_SI’s first model: a fast local coding model built with our automated research system, the Eureka Machine.
• Best-in-class fast local coding
• Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark
• Built from scratch in under 4 months
Private, unmetered intelligence on your own machine. Open weights available now on @huggingface, run it locally on your favorite framework or try it for free on @opencode or @pidotdev.
Xinyu Yang retweeted
🚀 Introducing DeepInfer, our in-house inference framework, now fully open source!
Powering DeepSeek-V4.1-Flash at scale with high throughput and low latency. We’re sharing our production stack to help the community lower compute costs, reduce energy use, and make AI more affordable.
🔗 github.com/deepseek-ai/DeepI…
1/4
congrats :)
MiniMax M3.1 Flash: Space Performance + Bunny Speed
🚀🐰 Yes, Space Bunny - which has topped global leaderboards with 200T+ tokens - was an earlier version of M3.1 Flash.
💰Redefining the performance economics of agentic AI. M3.1 Flash achieves frontier-level intelligence across key tasks such as Cowork (Automation-Bench: 62%; ALE: 30%), Coding (DeepSWE: 74%) and Visual Reasoning (BabyVision: 91%), at 1/3 the cost of M3 and 3x the speed.
API pricing (flat rate): $0.15 / $0.60 per 1M input/output tokens, with cache reads at $0.015 / 1M tokens.
Model weights will arrive on Oct 23.
API: platform.minimax.io
M Plan: minimax.io/m-plan
MiniMax Code for desktop: agent.minimax.io/download



























