Skip to main content
Angestrom home
SearchPapersModelsLive AIIntelligence
Search⌕⌘K
EnterprisePricingSign in

Stay Ahead in the AI Revolution

Weekly digest — EPI pulse, top intelligence, fresh lineage. Free, no account.

Follow Angestrom
Global source network
Synced every 5 minutes

Continuous sync from primary AI sources — indexed, enriched, and queryable in real time.

arXivHugging FaceGitHubOpenAIAnthropicDeepMindReutersBBC TechHacker NewsReddit MLVerified feedsFunding
ANGESTROM

The Intelligence Layer of Humanity. Everything AI. All in One Place.

Angestrom connects every piece of the AI ecosystem — data, models, research, companies, tools, and people.

info@angestrom.comwww.angestrom.comLucknow, Uttar Pradesh, India

Product

  • AI Search
  • AI Models
  • Research Papers
  • Companies
  • News & Events
  • GitHub Explorer
  • APIs & Tools
  • Datasets
  • Benchmarks
  • Model lifecycle
  • Funding graph
  • Contributors
  • AI Agents

Resources

  • Weekly digest
  • Documentation
  • Tutorials
  • Guides
  • News
  • Help / Start
  • Community

Company

  • About
  • Contact
  • Privacy Policy
  • Terms of Service
  • Acceptable Use

Enterprise

  • Pricing
  • Workspace
  • Contact Sales

Developer

  • Developer Hub
  • API docs
  • GitHub

Learn

  • Learning Academy
  • Roadmaps
  • Glossary
  • AI for Beginners

Popular Topics

Loading topics…
View All Topics →
© 2026 Angestrom Intelligence Private Limited. All rights reserved.
English
Theme
Angestrom home
SearchPapersModelsLive AIIntelligence
Search⌕⌘K
EnterprisePricingSign in
  1. Home
  2. /Repositories
  3. /abhisadineni/vllm
Read original ↗
repoGitLabTrust 82 · PrimaryPublished 6d agoLive · 6d ago

abhisadineni/vllm

A high-throughput and memory-efficient inference and serving engine for LLMs

Lineage graph

Paper → model → repo connections mined from source citations (Tier-1 exact match).

Why these links exist

Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.

  • PossiblePossibly related (embedding) · 60%OpenAI and Broadcom unveil LLM-optimized inference chip →
  • PossiblePossibly related (embedding) · 60%OpenAI and Broadcom announce chip designed for LLM inference at scale →
  • PossiblePossibly related (embedding) · 53%Evaluate a model properly →
  • PossiblePossibly related (embedding) · 52%FreqDepthKV: Frequency-Guided Depth Sharing for Robust KV Cache Compression in Long-Context LLM Inference →
  • PossiblePossibly related (embedding) · 52%Hardware startup unveils inference accelerator →
  • PossiblePossibly related (embedding) · 48%Cloud-vLLM Benchmark Differences [R] →

Covers

newsOpenAI and Broadcom unveil LLM-optimized inference chipnewsOpenAI and Broadcom announce chip designed for LLM inference at scalenewsHardware startup unveils inference accelerator

Related to

tutorialEvaluate a model properly

Implements

paperFreqDepthKV: Frequency-Guided Depth Sharing for Robust KV Cache Compression in Long-Context LLM Inference

Covers (incoming)

newsCloud-vLLM Benchmark Differences [R]

Related across the graph

newsOpenAI and Broadcom announce chip designed for LLM inference at scalenewsCloud-vLLM Benchmark Differences [R]newsOpenAI and Broadcom unveil LLM-optimized inference chippaperFreqDepthKV: Frequency-Guided Depth Sharing for Robust KV Cache Compression in Long-Context LLM InferencetutorialEvaluate a model properlynewsHardware startup unveils inference accelerator
Knowledge path·NOpenAI and Broadcom announce chip designed for LLM inference at scale→NCloud-vLLM Benchmark Differences [R]→NOpenAI and Broadcom unveil LLM-optimized inference chip→Rabhisadineni/vllm

Topics

gitlabopen-source

Explore

Search similar →Knowledge graph →All repos →Full intelligence feed →
Graph trust82Primary