AITOPAI热报
Industry

Anyscale 将出席 Microsoft Ignite，分享企业自建基础模型思路

Anyscale@anyscalecompute
Post· 2 sources
View the post
Why we picked it

Anyscale 在 Microsoft Ignite 有个演讲，讲企业怎么用开源模型加自家数据在自己服务器上训练自己的基础模型。如果你在纠结买 API 还是自建模型，11 月 18 日在 Moscone 中心可以听听他们的方案。

Anyscale 宣布参加 11 月 17-20 日在旧金山 Moscone 中心举办的 Microsoft Ignite 大会，展位号为 5389L。团队将在 11 月 18 日进行编号为 THR900 的演讲场次。其核心观点是下一波基础模型将由企业推出：每家公司都应该至少拥有一个自己可控的基础模型，采用开源模型、基于自有数据做 post-training、部署在自有基础设施上。

Image · Anyscale
Body

Anyscale 主张的模式是开源模型加企业自有数据微调加自建基础设施的组合，强调模型的所有权控制权留在企业手中，而非依赖闭源 API。

这一定位与 Anyscale 作为 Ray 与分布式训练平台供应商的角色一致，也与其长期推动开源模型在企业落地的方向相符。对正在评估自建模型与调用闭源 API 两条路线的技术决策者来说，该场次提供了一个具体框架。

Original · Anyscale

📍Nov 17-20 we're at Microsoft Ignite, Moscone SF. Booth 5389L.

The next wave of foundation models is coming from enterprises. Every company needs at least one FM it controls: open model, post-trained on your data, on your infra.

Session Nov 18 (THR900). Learn more: https://t.co/qsBmohpyzp

Industry Coordinates & Source Verification

This report is categorized under AITOP's "Industry" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include Anyscale, Microsoft Ignite, 开源模型, 企业级AI.

Source attribution: curated from Anyscale (@anyscalecompute). Cross-source corroboration: 2 independent sources have covered this development (including PyTorch), confirming industry-wide signal strength.

#Anyscale#Microsoft Ignite#开源模型#企业级AI

Keep reading

Recent stories with the same tag or section
ModelsBuildersMulti-sourceSelected01:02
Anyscale trains GLM-5.3-Flash at 1M-token context for genomics

Long-sequence post-training on biological data is a rare public data point, showing that million-token fine-tuning is practical on 64 GPUs rather than a cluster of thousands. The open-source stack means teams doing AI for science or other long-document domains can reproduce the setup directly. GLM-5.3-Flash originates from Zhipu (Z.ai), so it also demonstrates a Chinese open-weight model being repurposed by US labs.

#GLM-5.3-Flash#Anyscale#Lila Sciences#fine-tuning
Event
Anyscale@anyscalecomputeSource
How-toBuildersSelected02:01
Anyscale session on LLM post-training, RLVR and SkyRL

The most useful part is the terminal-agent case study, which shows how evals, custom environments and RL infrastructure fit together in a real post-training pipeline rather than as isolated concepts. Engineers building agents or running reinforcement learning fine-tuning can use SkyRL and the async rollout workflow to structure their own training runs. The OPD and MOPD sections also outline distillation methods rarely covered in tutorials.

#SkyRL#Anyscale#RLVR#GRPO
Anyscale@anyscalecomputeSource