Technology · Framework
vLLM
Framework · Catalog tech@1
High-throughput LLM inference and serving engine.
- Categories
- Matched as
- vllm
Momentum
Each signal is ranked as a percentile among the technologies that have it. A signal without enough history is not counted, and never shown as zero. How momentum works
Momentum
Collecting0 of 5 signalsNo score yet. Momentum is published only when at least 2 of the 5 signals have history. vLLM has 0, so there is no single number: each signal is shown on its own below. It is not a zero, and nothing is estimated in its place.
Snapshot OCT 24, 2026 · Scoring tech@1
- Official repository stars
- Collecting
- Star growth needs repository snapshots 30 days apart.93,061 stars now
- Member repository stars
- Collecting
- Star growth needs repository snapshots 30 days apart.0 new members in 30 days
- Package adoption
- Not published by its registries
- Its packages’ registries publish no adoption figure, so there is nothing to grow.
- Open models
- No official models
- No official model on Hugging Face, so this signal does not apply.
- News
- Collecting
- News counts once there are 3 events across the last 60 days; it has 2.2 in the last 30 days · 0 in the 30 before
Adoption by registry
The growth of vLLM’s own packages, one row per registry in that registry’s own unit. Registries count different things over different windows, so they are never added together.
No registry has two observations of vLLM’s own packages about 30 days apart yet, so there is no growth to show. Nothing is estimated in its place.
News activity
- Events · 7 days
- 1published events that mention it
- Events · 30 days
- 2published events that mention it
- Previous 30 days
- 0events in the 30 days before
Momentum history
Momentum history starts OCT 8, 2026. A chart appears once two daily snapshots carry a score; none does so far.
Repositories
Official 1
- vllm-project/vllm
A high-throughput and memory-efficient inference and serving engine for LLMs
93.1K stars
Members 33repositories tagged with one of its aliases, most-starred first
- ovg-project/kvcached
Virtualized Elastic KV Cache for Dynamic GPU Sharing and Beyond
1.5K stars
- kubeai-project/kubeai
AI Inference Operator for Kubernetes. The easiest way to serve ML models in production. Supports VLMs, LLMs, embeddings, and speech-to-text.
1.3K stars
- ModelCloud/GPTQModel
LLM model quantization (compression) toolkit with HW acceleration support for Nvidia, AMD, Intel GPU and Intel/AMD/Apple CPU via HF, vLLM, and SGLang.
1.3K stars
- bricks-cloud/BricksLLM
🔒 Enterprise-grade API gateway that helps you monitor and impose cost or rate limits per API key. Get fine-grained access control and monitoring per user, application, or environment. Supports OpenAI, Azure OpenAI, Anthropic, vLLM, and open-source LLMs.
1.2K stars
- verl-project/verl-omni
Multimodal RL training framework for diffusion & omni models
1.2K stars
- jmaczan/tiny-vllm
Build your own high performance LLM inference engine in C++ and CUDA - a smaller version of vLLM
1.1K stars
- prometheus-eval/prometheus-eval
Evaluate your LLM's response with Prometheus and GPT4 💯
1.1K stars
- nokia-applied-research/AnyJev
Turn any LLM into a Jev-style decision model: typed decisions, real probabilities, no training. (continue updating, welcome any issue and PR request)
1K stars
- Tencent-Hunyuan/UniRL
UniRL is a Framework for Unified Multimodal Model Reinforcement Learning
1K stars
Packages
“Is” packages are vLLM itself; ecosystem packages depend on them. The two are counted separately and never added together. Adoption is each registry’s own figure, with its percentile within that registry.
Is 11the technology itself, most-adopted first
- ai-dynamo-vllm
A high-throughput and memory-efficient inference and serving engine for LLMs
Not published by PyPI - nextai-vllm
A high-throughput and memory-efficient inference and serving engine for LLMs
Not published by PyPI - vllm
A high-throughput and memory-efficient inference and serving engine for LLMs
Not published by PyPI - vllm-acc
A high-throughput and memory-efficient inference and serving engine for LLMs
Not published by PyPI - vllm-cpu
A high-throughput and memory-efficient inference and serving engine for LLMs
Not published by PyPI - vllm-cpu-amxbf16
vLLM CPU inference engine (AVX512 + VNNI + BF16 + AMX optimized)
Not published by PyPI - vllm-cpu-avx512
vLLM CPU inference engine (AVX512 optimized)
Not published by PyPI - vllm-cpu-avx512bf16
vLLM CPU inference engine (AVX512 + VNNI + BF16 optimized)
Not published by PyPI - vllm-cpu-nightly
A high-throughput and memory-efficient inference and serving engine for LLMs
Not published by PyPI - vllm-tpu
A high-throughput and memory-efficient inference and serving engine for LLMs
Not published by PyPI - vllm-xft
A high-throughput and memory-efficient inference and serving engine for LLMs
Not published by PyPI
Ecosystem 7packages that depend on it, most-adopted first
- bentoml-unsloth
BentoML: The easiest way to serve AI apps and models
Not published by PyPI - docext
Onprem information extraction from documents
Not published by PyPI - hipporag
A powerful graph-based RAG framework that enables LLMs to identify and leverage connections within new knowledge for improved retrieval.
Not published by PyPI - hipporag-nyabot
A powerful graph-based RAG framework that enables LLMs to identify and leverage connections within new knowledge for improved retrieval.
Not published by PyPI - ochat
An efficient framework for training and serving top-tier, open-source conversational LLMs.
Not published by PyPI - octotoolkit
An effective and easy-to-use agentic framework with extendable tools for complex reasoning.
Not published by PyPI - orpheus-speech
Orpheus Text-to-Speech System
Not published by PyPI
Latest news the 3 latest events that mention it
- 8h agoOCT 24, 2026
- 21d agoOCT 3, 2026
- 4mo agoJUL 8, 2026