Alternatives to vLLM

free · inference-server tools positioned as replacements for vLLM. Open a matrix to compare side by side.

Dynamo-Triton

An open-source inference server designed to deploy AI models across major frameworks (TensorRT, PyTorch, ONNX, OpenVINO, Python, and RAPIDS FIL) on GPUs, CPUs, and accelerators.

model-servinginference-servermlops

AFIT verdict

Dynamo-Triton (formerly NVIDIA Triton Inference Server) is a robust option for developers needing to deploy and scale AI models in production environments. It supports multiple frameworks and hardware architectures, integrating well with Kubernetes and Prometheus. The main consideration is the complexity of setup and configuration for specific workloads.

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BentoML

inference-server · model-packaging

An open-source framework and platform designed for packaging, deploying, and scaling AI/ML models and custom inference pipelines in production.

inferencemodel-servingllm-inference

AFIT verdict

BentoML is a robust choice for AI developers and DevOps teams looking to deploy machine learning models and complex compound AI systems. It provides developer-friendly Python APIs, supports self-hosting for data sovereignty, and excels in scalability with features like scale-to-zero and GPU optimization. The primary catch is that while the core framework is open-source, advanced cloud orchestration, access to managed GPU hardware, and enterprise support require a paid subscription to Bento Cloud or their proprietary Inference Platform.

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Ollama

inference-server · local-run

A tool that enables running open large language models locally and scaling to the cloud.

local-llmmodel-inferencecloud-models

AFIT verdict

Ollama is highly suitable for developers looking to run open LLMs locally with simple CLI installation, with options to scale to cloud hardware for larger models. The local option ensures complete privacy as data is not trained on, though advanced cloud features require a paid subscription.

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