The Best ANEE Alternatives in 2026

PowerInfer supports serving large language models for local deployment and inference.

bitsandbytes enables large language models in PyTorch using k-bit quantization.

CTranslate2 provides a C++ and Python inference engine for transformer models.

NVIDIA Triton Inference Server provides an inference solution for deploying models in cloud and edge environments.

Sonar provides large-scale inference for language models through the project hosted in this repository.

NobodyWho runs language models locally through an inference engine designed for efficient operation across devices.

PrivateGPT provides an API layer for local model based AI applications, including RAG, tools, MCP, and text to SQL.

GPUStack manages GPU clusters for serving AI models and provides on demand GPU instances accessible through SSH.

Jina Serve helps developers build multimodal AI applications with cloud-native tools.

OpenLLM runs open-source language models as cloud API endpoints compatible with OpenAI clients.

For Apple Silicon, Rapid-MLX offers an open-source LLM inference server and Mac app compatible with OpenAI and Anthropic APIs.

Run image model inference with FLUX through the official repository from Black Forest Labs.

LLMKube runs self hosted LLM inference across heterogeneous GPU fleets using Kubernetes and several model runtimes.

Deploy generative and predictive AI models on Kubernetes with KServe’s distributed inference platform.

BitNet provides an inference framework for one-bit large language models.

A Rust inference engine for GGUF models, Shimmy uses WebGPU and supports the OpenAI API.

KServe provides distributed inference for generative and predictive AI models on Kubernetes.

A lightweight, portable pure C99 onnx inference engine for embedded devices with hardware acceleration support.

MindSpore provides a deep learning framework for training and inference across mobile, edge, and cloud scenarios.

FedML supports distributed model training, model serving, and federated learning, with a scheduler for AI jobs across cloud and local clusters.

This Kimi API guide explains model inference billing, including token charges and cache discounts.