The Best Kserve Alternatives in 2026

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

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

GraphPipe provides a protocol and software for deploying machine learning models separately from their frameworks.

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

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.

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

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

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

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

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

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

ANEE wraps pretrained transformer inference with an energy budget and early exits inside model execution.

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

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

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

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

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

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

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

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