IBM And Partners Launch Distributed AI Inference Tool
The llm-d version 1.0 framework helps enterprises manage AI workloads and reduce costs across infrastructure.
Updated on Oct. 20, 2026 in Quantum Computing
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IBM, Red Hat, and Google have released version 1.0 of the llm-d distributed AI inference framework. This project aims to help enterprises manage competing workloads, hardware efficiency, and software updates across their own infrastructure.
Why it matters
Enterprises require operational readiness to manage hardware failures, software updates, and competing user workloads. The framework aims to reduce inference costs and infrastructure dependence on single providers.
The framework runs 3,000 software-engineering agents concurrently and achieves 97% cache reuse in agentic tests. It utilizes a routing system to manage memory between GPU, CPU, and storage while supporting vLLM and SGLang engines.
The players
IBM
IBM is a leading global technology company that specializes in hardware, software, and cloud-based AI services.
Red Hat
Red Hat is an enterprise software company known for providing open-source products to the business community.
Google is a major multinational technology firm focused on internet-related services and artificial intelligence research.
Cloud Native Computing Foundation
This organization hosts the llm-d project as a sandbox initiative to foster open-source cloud native development.
The details
The project uses a routing system that directs requests to replicas holding cached context, separating prompt processing from response generation. It enables cached information to move fluidly between GPU memory, CPU memory, and storage, while supporting quality of service across multiple tenants.
Timeline
The llm-d project was co-founded in 2025.
Version 1.0 of the framework was released on October 20, 2026.
The project was highlighted at the PyTorch Conference North America in San Jose on October 20-21, 2026.
The Tech Race
The framework integrates into the broader Cloud Native Computing Foundation project ecosystem to standardize how AI workloads are deployed. It represents a shift away from closed-source hosted services toward open-source, portable infrastructure management.
Developers and enterprises can expect lower inference costs and better performance when managing large-scale AI agents. The framework provides the granular controls necessary to optimize hardware efficiency for those running AI workloads on enterprise infrastructure.
The takeaway
Open-source frameworks like llm-d allow companies to maintain greater control over their AI infrastructure and reduce vendor lock-in. Adopting these tools helps maintain operational readiness while significantly lowering the cost of deploying large-scale AI agents.
Further reading
Learn more about the latest innovations in high-performance model infrastructure on our Quantum Computing section page.
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Is it a good time for organizations to shift AI operations to their own infrastructure?
