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自托管版本

最近我们做了一个新项目 LLMPod ,定位是开源的私有化 LLM 推理服务平台,目前正在招募首批体验用户。 做这个项目主要是因为自己部署大模型之后,会发现“把模型跑起来”其实只是第一步。 后面还有 GPU 节点管理、推理引擎、模型部署、API Gateway 、故障切换、API Key 、配额、调用日志以及多用户权限等一系列问题。 LLMPod 希望把这部分能力整合起来。 目前主要包括: 接入和管理自己的 GPU 节点; 可视化部署、管理模型; 支持 vLLM / SGLang 等推理引擎; 将模型发布为 OpenAI 兼容 API ; 统一管理外部模型 API ; API Key 、配额、用量及调用日志管理; 路由、权重和 Failover 。 开源自托管版本可以免费使用。 目前项目还处在比较早期的阶段,所

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First observed in our collection
Oct 27, 2026, 8:30 AM UTC
Earliest cited publication
Oct 27, 2026, 8:14 AM UTC
Last observed
Oct 27, 2026, 8:30 AM UTC

Evidence in the last complete seven days

Source mentions
0
Previous seven days 1
Distinct sources
0
Previous 1
Comparable growth
Not comparable
Ranking appearances
0

Collection observations and undated records are separate from dated publication counts. Observed in 7d:0 · Undated records:0

Collection coverage · Limited historyCurrent / previous observed days 7 / 4

Counts use complete UTC days. First observed means first seen by this collection, not when a term was coined.

Current window:2026-10-30 → 2026-11-06 (end excluded) · History 12 days

Observed source coverage by seven-day window
SourceCurrent daysPrevious daysLast success
arXiv64Nov 6, 2026, 12:30 AM UTC
arXiv73Nov 6, 2026, 5:15 AM UTC
Ask HN74Nov 6, 2026, 3:45 PM UTC
Ask HN74Nov 6, 2026, 3:30 PM UTC
Ask HN74Nov 6, 2026, 4:00 PM UTC
Ask HN74Nov 6, 2026, 4:00 PM UTC
GitHub74Nov 6, 2026, 12:45 PM UTC
GitHub Issues74Nov 6, 2026, 4:00 PM UTC
GitHub Releases72Nov 6, 2026, 12:15 PM UTC
GitHub74Nov 6, 2026, 2:30 PM UTC
GitHub Issues74Nov 6, 2026, 4:45 PM UTC
GitHub Releases74Nov 6, 2026, 4:45 PM UTC
GitHub74Nov 6, 2026, 2:30 PM UTC
GitHub Issues74Nov 6, 2026, 5:00 PM UTC
GitHub Releases74Nov 6, 2026, 4:45 PM UTC
GitHub Discovery10Nov 6, 2026, 4:00 PM UTC
GitHub Trending74Nov 6, 2026, 1:15 PM UTC
Google73Nov 6, 2026, 8:30 AM UTC
Hugging Face73Nov 6, 2026, 8:30 AM UTC
Hugging Face Spaces10Nov 6, 2026, 3:30 PM UTC
Hacker News74Nov 6, 2026, 1:15 PM UTC
Hacker News74Nov 6, 2026, 4:15 PM UTC
Hacker News74Nov 6, 2026, 4:15 PM UTC
Hacker News10Nov 6, 2026, 4:00 PM UTC
Show HN10Nov 6, 2026, 3:45 PM UTC
MIT News73Nov 6, 2026, 5:45 PM UTC
OpenAI74Nov 6, 2026, 11:45 AM UTC
Product Hunt74Nov 6, 2026, 1:15 PM UTC
量子位64Nov 4, 2026, 10:15 AM UTC
OpenRouter10Nov 6, 2026, 5:34 PM UTC
Hugging Face Models10Nov 6, 2026, 5:34 PM UTC
HF Daily Papers10Nov 6, 2026, 5:34 PM UTC
OpenRouter10Nov 6, 2026, 5:34 PM UTC
SEC EDGAR74Nov 6, 2026, 10:45 AM UTC
Sequoia Capital73Nov 6, 2026, 6:45 AM UTC
Simon Willison10Nov 6, 2026, 3:15 PM UTC
TechCrunch74Nov 6, 2026, 3:00 PM UTC
Contracts Finder74Nov 6, 2026, 8:00 AM UTC
V2EX74Nov 6, 2026, 3:00 PM UTC
  • Counts describe monitored public sources, not the whole web or search demand.
  • Both weeks need adequate, comparable collection coverage.

Daily history · up to 90 days

Bars show dated source mentions. The latest UTC day may be incomplete. Zero mentions and missing collection coverage are different conditions.

2026-10-232026-11-06
View accessible daily data
Daily source mentions and collection observations
UTC dateMentionsSourcesObservationsRanking appearances
2026-10-230000
2026-10-240000
2026-10-250000
2026-10-260000
2026-10-271110
2026-10-280000
2026-10-290000
2026-10-300000
2026-10-310000
2026-11-010000
2026-11-020000
2026-11-030000
2026-11-040000
2026-11-050000
2026-11-060000
Source reference for [推广] 做了一个开源的 LLM 私有化部署平台 LLMPod,想找一些有 GPU 的朋友参与测试. Screenshot not yet available.
Source record · preview pending · v2ex.comOpen the source for the full page.

Source evidence

Sources in the evidence shown:V2EX 1

  1. V2EX · Published Oct 27, 2026

    [推广] 做了一个开源的 LLM 私有化部署平台 LLMPod,想找一些有 GPU 的朋友参与测试

    最近我们做了一个新项目 LLMPod ,定位是开源的私有化 LLM 推理服务平台,目前正在招募首批体验用户。 做这个项目主要是因为自己部署大模型之后,会发现“把模型跑起来”其实只是第一步。 后面还有 GPU 节点管理、推理引擎、模型部署、API Gateway 、故障切换、API Key 、配额、调用日志以及多用户权限等一系列问题。 LLMPod 希望把这部分能力整合起来。 目前主要包括: 接入和管理自己的 GPU 节点; 可视化部署、管理模型; 支持 vLLM / SGLang 等推理引擎; 将模型发布为 OpenAI 兼容 API ; 统一管理外部模型 API ; API Key 、配额、用量及调用日志管理; 路由、权重和 Failover 。 开源自托管版本可以免费使用。 目前项目还处在比较早期的阶段,所

    View source record →

Review priority 12 / 100

This score orders research candidates. It is not search volume, a forecast or verified commercial demand.

  • Evidence

    0 / 30

    Distinct dated articles or first observations; repeat crawling adds nothing.

  • Publisher diversity

    0 / 25

    Conservative owner grouping; syndicated copies count once.

  • Comparable change

    0 / 20

    Comparable complete weeks only; no percentage from a zero baseline.

  • Recent evidence

    3.9 / 15

    Latest publication or first observation, not repeated fetch time.

  • Name specificity

    8 / 10

    Explicit entities and technical concepts outrank ambiguous acronyms.

Limitations and interpretation

  • First observation is a collection milestone, not proof that a word or product is new to the world.
  • Counts describe monitored public sources, not the whole web or search demand.
  • First observed here is not the invention date of a name or idea.
  • Candidates come from public titles, summaries and entity URLs, not verified full-text analysis.
  • Owner grouping is conservative; undisclosed syndication may remain.
  • Both weeks need adequate, comparable collection coverage.
  • The previous week is too small for a reliable percentage change.
  • Current evidence comes from one publisher or owner.

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