A Comprehensive Showdown: OpenShift AI llm-d vs vLLM vs Ollama for LLM Inference Engines
infrastructure#llm📝 Blog|Analyzed: Apr 12, 2026 00:00•
Published: Apr 11, 2026 23:51
•1 min read
•Qiita AIAnalysis
This article offers a highly valuable and timely comparison of three major LLM Inference engines, shedding light on the best tools for different development and deployment stages. It brilliantly breaks down complex technical concepts like PagedAttention and Continuous Batching, making it easier for developers to optimize their AI infrastructure. The introduction of platforms like llm-d on OpenShift AI highlights an exciting leap forward in enterprise-grade Scalability and distributed processing!
Key Takeaways
- •Ollama is highlighted as an incredibly simple, single-binary tool perfect for local development, prototyping, and easy integration with interfaces like OpenWebUI.
- •vLLM shines in production environments requiring high throughput and memory efficiency, utilizing innovative PagedAttention and Continuous Batching.
- •llm-d emerges as a powerful distributed Inference platform on Kubernetes via OpenShift AI, expanding enterprise options with advanced routing and data transfer capabilities.
Reference / Citation
View Original"LLM（大規模言語モデル）を本番環境で運用する際、推論エンジンの選択は重要なポイントの一つかと思います。2025年後半から2026年にかけて、Red HatがOpenShift AI上でllm-dをGA（一般提供）したことで、エンタープライズ向けの選択肢が広がってきているようです。"
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