Agents
How distributed inference keeps large AI models moving
Distributed inference splits a large AI model into sub‑components that run concurrently on multiple compute nodes, allowing each node to process only part of the workload. This parallelism boosts thro
Distributed inference splits a large AI model into sub‑components that run concurrently on multiple compute nodes, allowing each node to process only part of the workload. This parallelism boosts throughput and reduces latency while lowering operational costs by balancing requests across heterogeneous GPUs and CPUs. IBM’s LLM‑D platform exemplifies this approach, demonstrating that even complex tasks such as air‑traffic control inference can be executed efficiently at scale.
Related
- End-to-End Latency-Minimizing and Load-Balanced Request Scheduling for Edge LLM Inference in Agentic AI Services
- Speculative Macro Commit for Faster Tool-Using Agents
- MiniCache: Reusable Program Caching with Small Model Interfaces for Efficient LLM Inference
Source: IBM Research | 2026-10-12