+
+

Related Products

  • Vertex AI
    961 Ratings
    Visit Website
  • Perplexity Computer
    26 Ratings
    Visit Website
  • JetBrains Junie
    12 Ratings
    Visit Website
  • RunPod
    205 Ratings
    Visit Website
  • Google AI Studio
    11 Ratings
    Visit Website
  • Fraud.net
    56 Ratings
    Visit Website
  • LM-Kit.NET
    26 Ratings
    Visit Website
  • Qloo
    23 Ratings
    Visit Website
  • ManageEngine EventLog Analyzer
    208 Ratings
    Visit Website
  • Bidtracer
    39 Ratings
    Visit Website

About

Luminal is a machine-learning framework built for speed, simplicity, and composability, focusing on static graphs and compiler-based optimization to deliver high performance even for complex neural networks. It compiles models into minimal “primops” (only 12 primitive operations) and then applies compiler passes to replace those with device-specific optimized kernels, enabling efficient execution on GPU or other backends. It supports modules (building blocks of networks with a standard forward API) and the GraphTensor interface (typed tensors and graphs at compile time) for model definition and execution. Luminal’s core remains intentionally small and hackable, with extensibility via external compilers for datatypes, devices, training, quantization, and more. Quick-start guidance shows how to clone the repo, build a “Hello World” example, or run a larger model like LLaMA 3 using GPU features.

About

vLLM is a high-performance library designed to facilitate efficient inference and serving of Large Language Models (LLMs). Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry. It offers state-of-the-art serving throughput by efficiently managing attention key and value memory through its PagedAttention mechanism. It supports continuous batching of incoming requests and utilizes optimized CUDA kernels, including integration with FlashAttention and FlashInfer, to enhance model execution speed. Additionally, vLLM provides quantization support for GPTQ, AWQ, INT4, INT8, and FP8, as well as speculative decoding capabilities. Users benefit from seamless integration with popular Hugging Face models, support for various decoding algorithms such as parallel sampling and beam search, and compatibility with NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs, and more.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

ML infrastructure engineers and researchers seeking a tool offering a deployment framework for GPUs or heterogeneous devices

Audience

AI infrastructure engineers looking for a solution to optimize the deployment and serving of large-scale language models in production environments

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version
Free Trial

Pricing

No information available.
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Luminal
United States
luminalai.com

Company Information

vLLM
United States
vllm.ai

Alternatives

Alternatives

Deci

Deci

Deci AI
OpenVINO

OpenVINO

Intel

Categories

Categories

Integrations

Hugging Face
Database Mart
Docker
KServe
Kubernetes
Llama 3
NGINX
NVIDIA DRIVE
OpenAI
PyTorch
Thunder Compute

Integrations

Hugging Face
Database Mart
Docker
KServe
Kubernetes
Llama 3
NGINX
NVIDIA DRIVE
OpenAI
PyTorch
Thunder Compute
Claim Luminal and update features and information
Claim Luminal and update features and information
Claim vLLM and update features and information
Claim vLLM and update features and information