The Best LMFlow Alternatives in 2026

MII makes low-latency and high-throughput inference possible, powered by DeepSpeed.

DyNet provides tools for building dynamic neural networks and deep learning models.

GPT-NeoX implements model parallel autoregressive transformers on GPUs using Megatron and DeepSpeed libraries.

The colibri-core NLP tool and library works with n-grams, skipgrams, and other linguistic patterns.

Gymnasium provides a standard API for single agent reinforcement learning environments and related utilities.

DEAP provides a Python framework for prototyping evolutionary computation algorithms and testing new ideas.

Tensorforce provides a TensorFlow library for applied reinforcement learning tasks.

Quilt manages scientific data on AWS with versioned data packages for teams and AI.

einops provides readable tensor operations for PyTorch, JAX, TensorFlow, and other frameworks.

Run sandboxes, task queues, and custom model inference with ultrafast boot times, instant autoscaling, and a developer experience that just works.

Kubeflow provides a machine learning toolkit for deploying workflows on Kubernetes.

Hugging Face Text Embeddings Inference serves text embedding models through an inference solution.

Hyperopt supports distributed asynchronous hyperparameter optimization in Python.

CLIP-as-service provides scalable image and sentence embeddings, reasoning, and ranking with CLIP.

Tensors and Dynamic neural networks in Python with strong GPU acceleration.

OpenModelZ scales LLM inference on Kubernetes with engines including vLLM, SGLang, and LMDeploy.

Hopsworks provides a feature store and MLOps platform for building and operating production machine learning systems.

Colibri runs mixture-of-experts language models on local hardware, streaming experts from disk with a small C engine.

Glide provides an open model gateway for developing production generative AI applications.

Ollama runs language models locally and provides tools to work with model files and APIs.

Candle provides a minimalist Rust framework for machine learning model development and execution tasks.

ONNX-MLIR compiles ONNX models using MLIR compiler infrastructure for model deployment workflows.

The cONNXr project provides a pure C runtime for ONNX models intended for embedded devices.

The Darknet neural network framework is written in C and CUDA and supports CPU and GPU computation.