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What are the primary hardware bottlenecks I need to prepare for when scaling an in-house LLM inference infrastructure for a growing user base?
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What are the primary hardware bottlenecks I need to prepare for when scaling an in-house LLM inference infrastructure for a growing user base?
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LLM inference infrastructure hardware bottlenecks GPU memory bandwidth KV cache networking power cooling production deploymentGoogle ↗
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ChatGPT1 real searches · gpt-6
LLM inference infrastructure hardware bottlenecks GPU memory bandwidth KV cache networking power cooling production deployment Google ↗
- Designing distributed AI inference: Core concepts and scaling dimensions · developers.redhat.com
- Optimizing distributed AI inference: Advanced deployment patterns · developers.redhat.com
- NVIDIA Inference Reference Architecture · docs.nvidia.com
- What is LLM Inference? · ibm.com
- I/o for LLM inference: a survey of storage and memory bottlenecks | Artificial Intelligence Review · link.springer.com
- NVIDIA Inference Reference Architecture · docs.nvidia.com