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A throughput-oriented high-performance serving framework for LLMs
FlashInfer is a library of high-performance GPU kernels purpose-built for accelerating large language model inference. It provides optimized implementations for attention operations (including flash attention, page attention, multi-head latent attention, and cascade attention) using paged key-value caches, fused kernel composition, and just-in-time compilation. The library also includes specialized kernels for mixture-of-experts layers, block-scaled low-precision quantization (FP8, FP4), and distributed collective communication. What distinguishes FlashInfer is its fused all-reduce communicat
Automatically Discovering Fast Parallelization Strategies for Distributed Deep Neural Network Training
Petals is a decentralized framework and inference engine for running large language models across a peer-to-peer network. It enables the execution of models that exceed the memory of any single machine by splitting computations and model layers across a collaborative swarm of GPUs. The system functions as a collaborative compute network where participants share local GPU resources and host model weights. It supports distributed prompt-tuning to adapt massive models to specific tasks and allows for the establishment of private compute swarms to process sensitive data within restricted, trusted
OpenAI-compatible HTTP serving for diffusion language models. LLaDA-8B-Instruct and LLaDA-1.5 in v0.1. Dream-7B in v0.1.1 (issue #1).
The main features of ioptimizethings/dlmserve are: Inference Frameworks.
Open-source alternatives to ioptimizethings/dlmserve include: efeslab/nanoflow — A throughput-oriented high-performance serving framework for LLMs. flashinfer-ai/flashinfer — FlashInfer is a library of high-performance GPU kernels purpose-built for accelerating large language model inference.… flexflow/flexflow — Automatically Discovering Fast Parallelization Strategies for Distributed Deep Neural Network Training. fminference/flexgen — FlexGen is an inference engine for large language models designed for high-throughput execution on single or multiple… ggerganov/llama.cpp — llama.cpp is a high-performance C++ inference engine and runtime for executing large language models locally across… bigscience-workshop/petals — Petals is a decentralized framework and inference engine for running large language models across a peer-to-peer…