Red Hat's llm-d Splits LLM Inference in Two — And IBM Fusion HCI Makes It Stick
Everyone figured LLM serving would just scale by throwing more GPUs at monoliths. Red Hat's llm-d on IBM Fusion HCI flips that script, splitting inference brains for real enterprise muscle.
⚡ Key Takeaways
- llm-d separates prefill and decode phases for independent scaling and KV cache efficiency.
- EPP scheduler routes requests intelligently, prioritizing low-queue, high-cache pods.
- Real Prometheus metrics confirm benefits: halved latency, pinned GPU util on IBM Fusion HCI.
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Originally reported by Towards AI
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