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10月20日
ProductsBuildersMulti-sourceSelected08:42
SGLang Low-Latency Mode Launches with NVIDIA and AMD Support

The same serving stack now optimises both NVIDIA and AMD paths, giving operators a concrete reason to benchmark MI355X deployments against GB300 rather than defaulting to CUDA. Teams running agentic inference traces can enable the mode through SGLang and expect P90 latency gains without changing models. The plugin system also means third-party kernels — starting with GLM-5.3, K3, and MiniMax M3 — will arrive without waiting for a core release.

#SGLang#NVIDIA#AMD#DeepSeek-V4.1-Flash
Event
官方账号LMSYS Org (SGLang)@lmsysorg11 sourcesSource
ModelsUS vs ChinaMulti-sourceSelected00:01
MiniMax-M3.1-Flash scores 42 on Artificial Analysis Index

Efficiency is the headline: the model used ~189M output tokens versus 324M for GLM-5.3-Flash, so low cost comes from token frugality as much as pricing. Its 93% hallucination rate on AA-Omniscience means teams should add verification layers for factual workloads. An open-weights release on 23 October 2026 would make it directly comparable with DeepSeek's open models for ASEAN startups building on local hosting.

#MiniMax#MiniMax-M3.1-Flash#DeepSeek#GLM
Event
官方账号Artificial Analysis@ArtificialAnlys6 sourcesSource
10月19日
IndustryUS vs ChinaSelected23:52
NetEase LobsterAI releases second China office Agent user behaviour report

Cache hit rate alone is a poor proxy for cost: models with nearly identical hit rates showed a more than seven-fold gap in what users actually paid versus list price, making DeepSeek's pricing structure unusually favourable for long agent tasks. Teams deploying office agents in China can use the per-model effective-cost figures to pick cheaper backends, with LobsterAI reachable via desktop, WeChat and Feishu. The report is self-reported product data, so treat the absolute numbers as one product's view rather than market-wide evidence.

#LobsterAI#NetEase#DeepSeek#agents
Event
宝玉@dotey2 sourcesSource
ModelsBuildersSelected18:37
DeepSeek-V4.1-Flash-1017 builds full projects from scratch

Self-feedback reinforcement learning that targets whole-project output, not single-file answers, signals a shift in coding benchmarks toward multi-file, runnable deliverables. Developers evaluating open-weight models for end-to-end scaffolding can compare V4.1-Flash-1017 against Claude and GPT coding models on game and 3D tasks before committing to a stack.

#DeepSeek#V4.1-Flash-1017#coding assistants#reinforcement learning
AI Will@FinanceYF5Source
10月18日
ModelsUS vs ChinaSelected06:10
DeepSeek-V4.1-Flash-1017 lands on Ollama with US hosting

Zero-retention US hosting lowers the compliance barrier for teams that previously avoided Chinese models on data-residency grounds, letting Ollama Cloud serve as an evaluation path before any API commitment. Practitioners can test it immediately via 'ollama run deepseek-v4.1-flash:1017-cloud' or plug it into Claude Code and OpenCode as a cheaper per-task agent model. Against Claude models, the pitch is lower cost per completed task rather than raw capability.

#DeepSeek#Ollama#coding assistants#agents
ollama@ollamaSource
10月17日